Acta Astronautica 64 (2009) 988–1005www.elsevier.com/locate/actaastro

Image compression systems on board satellites�

Guoxia Yua,∗, Tanya Vladimirovaa, Martin N. Sweetinga,b

aSurrey Space Centre, University of Surrey, Guildford, Surrey GU2 7XH, UKbSurrey Satellite Technology Limited, Tycho House, 20 Stephenson Road, Surrey Research Park, Guildford GU2 7YE, UK

Received 31 October 2006; accepted 16 December 2008Available online 12 February 2009

Abstract

On-board image compression systems aim to increase the amount of data stored in the on-board mass memory and transmittedto the ground station. This paper presents an overview and analysis of the state-of-the-art in on-board image compression systems.Compression methods and systems implementations are reviewed. Statistical analysis and developing trends are given. A newarchitecture of an on-board image compression system for future disaster monitoring multi-satellite missions in LEO is described.© 2008 Elsevier Ltd. All rights reserved.

Keywords: Image compression; On-board; Compression system; Disaster monitoring; Space missions

1. Introduction

Space missions are designed to leave Earth’s atmo-sphere and operate in the outer space. Satellite imagingpayloads, mostly operate a store-and-forward mecha-nism, whereby the captured images are stored on boardand transmitted to ground later on. With the increase ofspatial resolution and swath, space missions are facedwith the necessity of handling an extensive amount ofimaging data. Two current missions are described be-low to illustrate the increased data volume and limitedresources on board.

The disaster monitoring constellation (DMC), devel-oped by Surrey Satellite Technology Limited (SSTL),is the first Earth observation (EO) constellation,

� This work was supported by a UK ORS grant and the Universityof Surrey.

∗Corresponding author.E-mail addresses: g.yu@surrey.ac.uk

(G. Yu), t.vladimirova@surrey.ac.uk (T. Vladimirova),M.Sweeting@SSTL.co.uk (M.N. Sweeting).

0094-5765/$ - see front matter © 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.actaastro.2008.12.006

consisting of five low-cost small satellites—AlSAT-1,BILSAT-1, NigeriaSat-1, UK-DMC and Beijing-1. TheDMC satellites jointly provide daily images to any pointon the globe for applications including disaster mon-itoring. DMC has been providing disaster relief orga-nizations routinely with images of areas stricken bydisasters such as the Asian Tsunami, Darfur, African lo-cust plague and the hurricane Katrina in the US. DMCsatellites offer 32m multispectral (MS) imaging, over a600km swath width in low-Earth orbit (LEO). Beijing-1, for example, combines the basic DMC payload witha 4m ground sampling distance (GSD) panchromatic(PAN) instrument, to provide continuity of DMC dataand to carry out systematic mapping at a higher res-olution. There are plans to advance the DMC conceptfurther. The DMC roadmap envisions developing high-resolution capability to eventually provide hourly ornear real-time surveillance [4,5]. All these technologi-cal advancements lead to an increasing data volume.

The recent TOPSAT satellite (launched in October2005), which was built with the SSTL micro-satelliteplatform, is designed to demonstrate capabilities of

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 989

low-cost small satellites for remote sensing usinghigh-quality optical imagery. A novel optical payloadprovides PAN images at 2.5m GSD with 8000 pix-els and MS images in three bands, each at 5m GSDwith approximately 2100 pixels. Each acquisition of asquare PAN image and a square MS image by TOPSATamounts to 600Mbits of data, however only four suchdata sets could be stored on board and the downlinkdata rate is 11Mbps [1–3].

Image compression compensates for the limitedon-board resources, in terms of mass memory anddownlink bandwidth and thus provides a solution tothe “bandwidth vs. data volume” dilemma of modernspacecraft. Therefore compression is becoming a veryimportant feature in payload image processing unitsof many satellites. A survey of on-board compressionsystems developed exclusively by the French SpaceAgency (CNES) is given in [6]; however, there is noliterature source that gives a systematic overview ofexisting image compression systems across a larger setof space missions.

This paper aims to review and analyse all satellitecompression systems described in the literature at thetime of writing of this paper. The paper is structured asfollows. Section 2 discusses image compression tech-niques and briefly introduces two compression recom-mendations published by the Consultative Committeefor Space Data Systems (CCSDS). Sections 3–5 reviewimage compression systems on board around 40 currentspace missions. Discussion and developing trends arethen given in Section 6. Section 7 presents an on-boardimage compression system for future disaster monitor-ing missions in LEO. Finally, conclusions are drawn inSection 8.

2. Overview of image compression techniques

Image compression methods are divided into twoclasses, lossless or lossy. With lossless image compres-sion, the reconstructed image is exactly the same as theoriginal one, without any information lost. The entropy,which measures the quantity of information containedin a source, gives a theoretical boundary for losslesscompression expressed by the lowest compression bit-rate per pixel. Entropy depends on the statistical natureof the source and ideally an infinite-order probabilitymodel is needed to evaluate it [7]. On the contrary, lossyimage compression would reconstruct the image with avarying degree of information loss.

There are several types of redundancy in an image,such as spatial redundancy, statistical redundancy, andhuman vision redundancy. Basically, removing these

types of redundancy is how the process of compressionis achieved.

(a) Spatial redundancy means that the pixel informa-tion could be partially deduced by neighbouringpixels. Spatial decorrelation methods, like predic-tion or transformation, are usually employed toremove the spatial redundancy. Prediction is usedto predict the current pixel value from neighbour-ing pixels. For example the differential pulse codemodulation (DPCM)method is a typical predictionbased technique. Transformation is used to trans-form the image from the spatial domain into an-other domain, applying, for example, the discretecosine transform (DCT) or the discrete wavelettransform (DWT).

(b) Statistical redundancy explores the probabilityof symbols. The basic idea is to assign shortcodewords to high-probability symbols, and longcodewords to low-probability symbols. Huffmanor arithmetic coding are two popular methods toremove statistical redundancy; they are usuallycalled entropy coding.

(c) Human vision redundancy, when dealing withlossy compression, explores the fact that eyes arenot so sensitive to high frequency. Removing hu-man vision redundancy is normally achieved byquantization, with high-frequency elements beingover quantized or even deleted.

A typical architecture of an image compressionsystem consists of spatial decorrelation, which is fol-lowed by quantization and finally entropy coding isperformed. According to different techniques used forspatial decorrelation, compression systems can be sep-arated into prediction, DCT and DWT-based systems.Prediction based compression methods include DPCM[8–9], adaptive DPCM [10], CCSDS lossless datacompression (CCSDS-LDC) [11], lossless JPEG [12]and JPEG-LS [13]. DCT-based compression methodsinclude JPEG-baseline [12] and specifically designedDCT compression methods. DWT-based compressionmethods include JPEG2000 [14], EZW [15], SPIHT[16], CCSDS image data compression (CCSDS-IDC)[17] and specifically designed DWT compressionmethods.

The block truncation coding (BTC) [18] algorithm isa special case of compression, which does not fit in thetypical architecture. The quantization is based on imagestatistical information, which is also forwarded as partof the compressed bit stream. This technique has beenmainly used in early missions of SSTL.

990 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

CCSDS is an international group dedicated to pro-viding technical solutions to common problems facedby member space agencies, such as NASA, ESA, etc.Two compression algorithms have been proposed byCCSDS. The first one is the CCSDS-LDC, which hasbeen widely used in many missions. The second one isthe CCSDS-IDC, which is a brand new recommenda-tion.

2.1. CCSDS-LDC

In May 1997, CCSDS published a recommendationstandard for lossless data compression, which is an ex-tended Rice algorithm, with added two low-entropy cod-ing options [19]. This recommendation addresses onlylossless source coding. It has widespread applicabilityto many forms of digital data. In particular, the sciencedata from many types of imaging or non-imaging in-struments are well suited for the application of this al-gorithm.

The algorithm consists of two separate functionalparts: a pre-processor and an adaptive entropy coder[11]. The pre-processor is used to decorrelate a blockof J sample data and subsequently map them into sym-bols suitable for the entropy coding stage. The entropycoding module is a collection of variable-length codesoperating in parallel on blocks of J pre-processed sam-ples. Each code is nearly optimal for a particular ge-ometrically distributed source [20]. The coding optionachieving the highest compression is selected for trans-mission, along with an ID bit pattern used to identifythe option to the decoder. Because a new compressionoption can be selected for each block, the algorithm canadapt to changing source statistics [11].

2.2. CCSDS-IDC

Since 1998, the CCSDS data compression workinggroup began to assess the feasibility of establishing animage compression recommendation suitable for spaceapplications and the CCSDS-IDC Blue Book was fi-nally produced in November 2005 [17]. The compres-sion technique described in this recommendation can beused to produce both lossy and lossless compression. Itsupports both frame-based input formats produced, forexample, by CCD arrays and strip-based input formatsproduced by push-broom type sensors. An image pixelresolution of up to 16bits is supported.

The compressor consists of two functional parts, aDWT module that performs decorrelation and a bit-plane encoder (BPE), which encodes the decorrelateddata. This architecture is similar to that of JPEG2000.

It differs from the JPEG2000 standard in several re-spects: (a) it specifically targets high-rate instrumentsused on board space missions; (b) a trade-off has beenperformed between compression performance and com-plexity with particular emphasis on space applications;(c) the lower complexity of this recommendation sup-ports fast and low-power hardware implementation and(d) it has a limited set of options, supporting its success-ful application without in-depth algorithm knowledge[17].

According to literature sources [21–24] CCSDS-IDCcould achieve performance similar to JPEG2000.

3. Current space missions with on-board imagecompression

A comprehensive survey of the literature has beencarried out as a result of which more than 40 spacemissions have been identified as having image com-pression on board. They have been reviewed, focusingon the on-board image compression systems, includingthe algorithms used and their implementations. Table 1gives a summary of the reviewed missions, where thename of the spacecraft is followed by the organizationand the launch year (or scheduled launch year). For ex-ample, “Tsinghua-1 (SSTL-THU2000)” means that theTsinghua-1 micro-satellite was built by SSTL, belongsto TsingHua University (THU), and was launched inyear 2000.

In Fig. 1, the application sectors of the investigatedmissions are shown. More than half of them (23 out of40) are EO satellites. Fig. 2 reveals that the reviewedcompression systems can be divided in four main classeswith respect to the theoretical basis of compression,with more than half of them being transform-based (i.e.DCT and DWT). The prediction-based techniques oc-cupy around 40% and can be further subdivided intoDPCM, LPEG-LS, lossless JPEG and Rice-based sys-tems. The BTC-based compression systems only holda small sector.

Details of prediction-based compression systems, andtransform-based compression systems are introduced inSections 4 and 5, respectively. As the compression sys-tems based on the BTC algorithm do not fit in any ofthe two main classes we review them below.

3.1. BTC-based compression systems

BTC is a type of lossy image compression techniquefor greyscale images. It divides the original images intosmall blocks, and then uses a quantizer, which adaptsitself according to the image statistics, to reduce the

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 991

Table 1Image compression systems on-board space missions.

Satellites Compression algorithms Theoretical basis Implementations Applications

PoSAT-1 (SSTL-Portugal1993) AMPBTC BTC T800 transputer EOTsinghua-1 (SSTL-THU2000) AMPBTC BTC T805 transputer EOTiungSAT (SSTL-Malaysia2000) Improved AMPBTC BTC T805 transputer EOSPOT-1 (CNES1986) Fixed-rate DPCM DPCM n/a EOSPOT-2 (CNES1990)SPOT-3 (CNES1993)SPOT-4 (CNES1998)IKONOS (USGeoEye1999) ADPCM Kodak DPCM ASIC-Kodak BWCP EOQuickBird (US DigitalGlobe2001) ADPCM Kodak DPCM ASIC-Kodak BWCP EOWorldView-1 (US DigitalGlobe2007) ADPCM Kodak DPCM ASIC-Kodak EOSTEREO (NASA2006) RICE and a lossy wavelet

(H-compress)RICE and DWT OBC-RAD6000 Sun Expl

MTI (US DOE2000) CCSDS-LDC RICE ASIC-USES chips EOMars Odyssey (NASA2001) CCSDS-LDC RICE ASIC-USES chips Mars Expl

fast lossless predictivecompressor or slowerlossy DCT compressor

DPCM or DCT OBC

EO-1 (NASA2000) CCSDS-LDC RICE ASIC-USES chips EOPICARD (CNES2009) CCSDS-LDC RICE DSP Sun ExplFedSAT (Australia2002) Adaptive JPEG-LS JPEG-LS FPGA-Xilinx XQR4062XL EOChang’E-1 (China2007) Differential Predictive+

Bit PlaneDPCM FPGA Moon Expl

Phobos (SovietUnion1988) DCT+scalar quantizer+fixed length coding

DCT OBC-Z80 Mars Expl

Clementine (NASA1994) DCT+Quantizer+ZigZag+RLE+Huffman (very closeto JPEG)

DCT ASIC-CNES ICM Moon Expl

Mars94/96 Probe (Russia) Mars ExplCassini Probe (NASA/ESA1997) Saturn ExplETS-7 (JAXA1997) Science DemoLunar A (JAXA2010) Moon ExplNozomi (JAXA1998) Mars ExplFUVIS SMEX (NASA) Science DemoSPOT-5 (CNES2002) DCT (with rate controlled) DCT n/a EOTRACE (NASA1998) JPEG-baseline DCT OBC (a custom

computer of AMD2910bit-slice architecture)

Sun Expl

Proba-2 (ESA2009) JPEG-baseline and LZW DCT OBC EOMicroLabSat (JAXA2002) JPEG-baseline and

Lossless JPEGDCT LosslessJPEG

OBC-64 bit RISC Science Demo

SUNSAT (SouthAfrica1999) JPEG-baseline DCT DSP-DSP56L002 EOTEAMSAT (ESA1997) JPEG-baseline DCT DSP-TCS21020 Science DemoPROBA-1 (ESA2001) JPEG-baseline DCT DSP-TCS21020 EOBeijing-1 (SSTL-China2005) JPEG-baseline DCT DSP EOALOS (JAXA2006) JPEG-baseline and

Lossless JPEGDCT LosslessJPEG

ASIC-JAXA IDCP EO

Solar-B (JAXA2006) 12bit JPEG-baselineand 12bit DPCM

DCT and DPCM ASIC Sun Expl

Meteisat-8 (ESA2002) JPEG-baseline andLossless JPEG

DCT LosslessJPEG

n/a Weather

Cartosat-1 (ISRO2005)Cartosat-2 (ISRO2007)

JPEG-baseline (CR = 3.2) DCT n/a EO

RapidEye (SSTL-Germany2008) Lossless and Lossy JPEG DCT Real-time EOTHEOS (CNES-Thailand2008) DCT (CR = 2.8 or 3.7) DCT n/a EOBilSAT-1 (SSTL-Turkey2003) JPEG2000 DWT FPGA+DSP−

(XCV300E+TMS320C6701)EO

RASAT (Turkey2009) JPEG2000 DWT FPGA-Xilinx VirtexII 1000 EOIMS-1 (ISRO2008) JPEG2000 DWT n/a EO

992 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

Table 1Continued.

Satellites Compression algorithms Theoretical basis Implementations Applications

Mars Exploration Rovers(NASA2003)

ICER and LOCO DWT and JPEG-LS OBC-RAD6000 Mars Expl

X-SAT (Singapore2009) Content-driven versionJPEG2000

DWT FPGA-(SA1110StrongARM(20)+ActelAX1000(2))

EO

PLEIADES-HR (CNES2010) DWT+BitPlaneEncoder DWT ASIC EO

Notes: EO—Earth observation; Demo—Demonstration and Expl—Exploration.

57%

10%

12%

10%

3%5% 3%

EOScience DemoMars ExplSun ExplWeather SATMoon ExplSaturnExpl

Fig. 1. Application type sectors for the space missions in Table 1.

BTC6%

DWT10% JPEG-LS

4%

Predict40%DCT

45%DPCM

19%Rice

10%

LosslessJPEG6%

Fig. 2. Theoretical basis of compression systems in Table 1.

number of grey levels in the image. Normally, a twolevel quantizer is used to create a binary bitmap of theimage. The compressed image consists of the mean,standard deviation and bitmap. The moment preservingnature of moment preserving BTC (MPBTC) comesfrom using three levels of compression. The choice ofwhich of these levels is used, is based upon the standarddeviation of the current block of pixels, by comparingit with two thresholds.

A form of adaptive block size, MPBTC (a-MPBTC)has been used on-board PoSAT-1 (SSTL-Portugal1993)

[25], Tsinghua-1 (SSTL-THU2000) [26] and TiungSAT(SSTL-Malaysia2000) [27]. The compression in all ofthem is implemented by software running on transput-ers, T800 or T805.

A system block diagram of PoSAT is shown in Fig. 3.When an image is captured by the Earth imaging system(EIS), it is sent to the transputers for processing andcompression. The image is then transferred to the on-board computer (OBC) via the satellite data bus calledSSTL data sharing network (DASH). The OBC stores allthe files in a filing system prior to transfer to ground. InTiungSAT and Tsinghua-1, a hybrid scheme, combiningcontroller area network (CAN) and DASH data bus, isused.

4. Prediction-based compression systems

This section reviews systems based on prediction-based techniques, with the exception of systems us-ing lossless JPEG, which are reviewed in Section 5.1.2“JPEG compression systems”. Prediction based tech-niques other than DPCM, from Rice to JPEG-LS, havebeen mostly used for lossless data compression.

4.1. DPCM compression systems

The first mission deploying an on-board image com-pression system is SPOT-1 (CNES1986). The compres-sion algorithm used is DPCM with a fixed compressionratio (CR). The following SPOT-2 to SPOT-4 missionsall use the same technique [6]. The first pixel of threeconsecutive pixels is considered as a reference pixel.The prediction of the other two pixels is computed as thehalf sum of their two adjacent reference pixels, insteadof the previous pixel. The difference is non-uniformquantized (the quantization law is adapted to the Lapla-cian distribution of the differences) and is coded using5bits for each difference. The reference pixels are notcompressed. Thus the number of bits per pixel at the

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 993

DASH 1DASH 0

2nd OBC

MUX

Transputer 1

Transputer 0

TIPE

=LinkAdaptor Star

GPS

Narrow

WideCCD

CCD

Sensor

Back-uplink

MUXVideoHybrid

ControlElectronics

A/D

EIS controlled by microcontroller

Ground

micro-controller

HC11

Image

BufferMemorey

MainOBC

Downlink

Fig. 3. Simplified system diagram of PoSAT [25].

Table 2Comparison of BWC, ABWC and EBWC ASICs developed by Kodak.

BWCP ABWC EBWC

Prediction X = �B+�C Median predictor Median predictorRate control A desired bit-rate Two desired bit-ratess Two desired bit-ratesCompression modes Two (fixed rate, a desired bit-rate) Three (BWC+lossless mode) Four (ABWC+pass through mode)

output of the DPCM is (8+2∗5)/3= 6, and the corre-sponding compression ratio is 1.33.

The Eastman Kodak Company developed a propri-etary rate-controlled adaptive DPCM (ADPCM) imagecompression algorithm for commercial remote sensingapplications. This algorithm has been implemented in aspace-qualified bandwidth compression (BWC) appli-cation specific integrated circuit (ASIC), called BWCP,which is used on board IKONOS (US.GeoEye1999) andQuickBird (US.DigitalGlobe2001). BWCP operates intwo modes, a fixed-rate output or a rate-controlled toa desired bit-rate output. The compression algorithmis a numerically-lossy technique and utilizes an adap-tive predictor, as well as an adaptive quantization strat-egy and Huffman-based encoding to achieve its com-pression ratio. A rate-controlling mechanism is used tomodify the compressor to generate essentially a fixedoutput bit-rate [28]. After BWCP, Kodak continued thedevelopment and successively produced the advancedBWC (ABWC) and the enhanced BWC (EBWC). All

of them are implemented as ASICs. ABWC has mi-grated from the Kodak adaptive prediction to the me-dian predictor that is used in JPEG-LS. A lossless modewas added to get the highest quality imagery. Besides, asecond compression ratio was added to make the ASICmore adaptive. EBWC incorporated two independentcompression modules within a slightly larger ASIC. Apass-through mode was added to provide raw, uncom-pressed data. Additionally, the parallel input and outputinterfaces were replaced by high-speed low-voltage dif-ferential signalling (LVDS) serial interface, so a phaselock loop (PLL) was also incorporated to re-synchronizeand sample the serial input data [28,29]. Comparisonof these three ASICs is shown in Table 2. WorldView-1 (US.DigitalGlobe2007) has a digital processing unit(DPU), designed and custom-built by Kodak. DPU pro-vides real-time radiometric/geometric calibration, be-sides Kodak ADPCM image compression [30]. Thecompression system on board Chang’E-1 (China2007)[88] adopted a bit-plane based differential predictive

994 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

RS-422 interfaceto spacecraftDate HandlingSystem

Actel A 1280Aantifuse FPGA

UT80C196KDmicrocontroller

Xilinx XQR4062FPGA

SRAM(1x8K)

SRAM(2 x 256K)

EEPROM(2 x 128K)

Flash RAM(2 x 4M)

Fig. 4. Block diagram of HPC-I on FedSAT [43].

coding algorithm with a compression ratio of 2 as aminimal value. The algorithm adapts itself automati-cally to perform lossy or lossless compression depend-ing on the image contents. It is implemented in a FieldProgrammable Gate Array (FPGA) chip.

4.2. Compression systems based on the Rice algorithm

Golomb coding, proposed by Solomon W. Golombin 1966, uses a tunable parameter m to divide the in-put value into two parts: q, the result of a division bym, and r, the remainder. Rice coding is equivalent toGolomb coding when the tunable parameter is a powerof two. This makes it extremely efficient for use in dig-ital world, since the division operation becomes a bit-shift operation and the remainder operation becomes abit-mask operation. The Rice algorithm, which consistsof the prediction and the Rice coding, was firstly pub-lished in 1971 [31]. In the following 20 years advancedvariations have been developed [32–34]. This algorithmhas excellent performance with low complexity for im-plementation.

The Rice algorithm is implemented in software onan OBC RAD6000 in STEREO (NASA2006), togetherwith a lossy wavelet image compression algorithm,called H-compress [41].

CCSDS-LDC, presented in Section 2.2, is an ex-tended Rice algorithm. Universal Source Encoding forScience Data (USES) is a space qualified ASIC withimplementation of CCSDS-IDC, produced by the Cen-tre for Advanced Microelectronics and BiomolecularResearch (CAMBR), University of Idaho [35]. Thereare a lot of missions, using in software or hardware im-plementations of CCSDS-LDC. For example, the com-

pression systems of MTI (US-DOE2000) [36], MarsOdyssey (NASA2001) [37] and EO-1 (NASA2000)[38,39], which use CCSDS-LDC, are implemented asASICs (i.e. USES). In contrast, CCSDS-LDC is im-plemented in software on a DSP processor in PICARD(CNES2009) [40]. A detailed table of these missions isgiven in [35].

4.3. Adaptive JPEG-LS compression system on FedSAT

FedSAT, (Australia2002) deployed an on-boardimage processing system—the satellite adaptive im-age compression system (SAICS). SAICS was de-signed and implemented on a radiation hardenedXilinx XQR4062XL SRAM-based FPGA, in a high-performance computing payload (HPC-I) shown in Fig.4. An UTMC 80196 microcontroller interfaces withthe spacecraft and provides local control and monitor-ing for the payload activities. It coordinates loading ofhardware configuration files into the FPGA chip andreports to the ground station about the operation sta-tus of the payload and other housekeeping data. Theoperating system of the microcontroller is stored in anEEPROM, while the flash memory stores configura-tion files. The SRAM stores commands and data andprovides a working area for the microcontroller. Thememory controller is implemented in an Actel anti-fuseFPGA managing the joint access of the FPGA and theSRAM to the microcontroller.

The proposed SAICS takes advantage of the con-trolled error described in the near lossless compres-sion algorithm of JPEG-LS. By adaptively varying theamount of error introduced into an image, it is possi-ble to significantly increase the compression ratio with-

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 995

HRSstereo

instrument

HRG 1instrument

HRG 2instrument

St (@128 Mb/s)

@45.5 Mb/s

@45.5 Mb/s

@45.5 Mb/s

HM1A (@128 Mb/s)

HM1A (@128 Mb/s)

HX1 (@104 Mb/s)

HX2 (@104 Mb/s)

HX2B (@128 Mb/s)

HX2A (@128 Mb/s)

X-Band

OMUX

Compression unit

Compressionchannel #1

Formatingchannel #1

Formatingchannel #2

Formatingchannel #3

Compressionchannel #2

Compressionchannel #3

Ancillary date

Solid State Mass Memory(90 Gbits - End Of Life)

Coding &Cipher. #1

Coding &Cipher. #2

Format, Coding & Cipher. unit

@ 50 Mb/s

@ 50 Mb/s

Transmitter channel #1

Transmitter channel #2

SSPA

SSPA QPSK

QPSK

SE

ECT

L

3/7

Fig. 5. On-board image chain of SPOT-5 [46].

out losing any information from the areas of an imagedeemed “important”. The SAICS is a low complexity,high-speed adaptive compression system that uses loss-less and near lossless techniques depending on localimage statistics [42–44].

5. Transform-based compression systems

5.1. DCT-based compression systems

“JPG” image files, compressed by JPEG-baseline, atypical DCT compression technique, have been widelyused on Internet. JPEG and other DCT-based compres-sion techniques have been employed in many space mis-sions.

5.1.1. Developments in CNESA DCT based algorithm with a scalar quantizer and

fixed length coding was implemented in one of the cam-era payloads of Phobos (1988). This algorithm with se-lectable CR of 4, 8 and 12 was implemented in softwareand operated off-line. The compression of one 208×144image lasted for around one hour due to the limited per-formances of the Z80 type microprocessor [6].

Between 1990 and 1991, the image compressionmodule (ICM) was developed as a DCT based imagecompression ASIC, under a CNES contract on up-to-date radiation tolerant technology. It is capable of4Mpixels/s real-time compression and is adapted for

compression ratios of about 3–20. ICM is based onDCT with a variable length coding algorithm very closeto ISO/JPEG standard. The DCT transform producesdecorrelated coefficients with 11bits precision. Thequantization step is defined by two parameters: a scalefactor (constant for all coefficients) and a weightingfactor which sets different quantizers for each coef-ficient. The quantized block is scanned in a zig–zagorder, then searched for zero values. Each non-zerovalues produces an event, which is Huffman coded us-ing a table with predefined variable length codes. ICMhas been selected by several space missions, includingClementine (NASA1994), Cassini probe (US/EU1997),ETS-7 technological satellite (Japan1997), Lunar A(Japan2010), Nozomi (known before launch as Planet-B, Japan1998), and FUVIS SMEX (NASA) [6].

The compression technique used on SPOT-5(CNES2002) is a variable rate DCT-based algorithmwith a rate control scheme, offering a fully suitableimage quality for users. This compression operates inreal time and delivers a fixed-rate bit-stream with a 2.8compression ratio for high-resolution geometric (HRG)and high-resolution stereoscopic (HRS) PAN bandsand 2.28 for HRG spectral bands. This algorithm isclose to JPEG-baseline with a flat quantization matrix.It is composed of a DCT decorrelator, a scalar uniformquantizer and an entropy coder [45–46]. The on-boardimage chain is depicted in Fig. 5. The compression unitis capable of processing simultaneously three outputs

996 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

(out of seven delivered by instruments). Each of thethree compression channels delivers a bit stream at afixed-rate of 45.5Mb/s in order to match the fixed rateof the payload telemetry channel at 50Mb/s. This bitstream is then formatted, potentially recorded in themass memory, encoded and ciphered in the formattingunit. The THEOS (CNES-Thailand2008) [89] is alsousing DCT-based algorithm with compression ratio of2.8 or 3.7.

5.1.2. JPEG compression systemsOn TRACE (NASA1998), the data handling com-

puter (DHC), a custom image processor based on theAMD 2910 bit-slice architecture, performs variouskinds of image processing, including JPEG-baselinecompression [47]. SUNSAT (SouthAfrica1999) [50],TEAMSAT (ESA1997) [51] Proba-1 (ESA2001) [52]and Beijing-1 (SSTL-China2005) all utilize DSP pro-cessor to run the JPEG-baseline compression software.Proba-2 (ESA2009) [48] andMicroLabSat (JAXA2002)[49] have data compression implemented in the OBC.Cartosat-1 (ISRO2005) and Cartosat-2 (ISRO2007)use the JPEG-baseline compression algorithm with acompression ratio of up to 3.2 [90]. Besides JPEG-baseline lossy compression, Proba-2, also employ theLempel–Ziv–Welch algorithm (LZW) for lossless datacompression, and MicroLabSat employ lossless JPEGfor lossless data compression. The payload electronicunit on board the RapidEye 5-satellite constellation(SSTL-Germany2008) [91] supports both selectablelossless (around 2:1) and lossy (up to 10:1) compres-sion for each band in real time.

The advanced land observing satellite (ALOS)(JAXA2006) flies an ASIC chip, called image datacompression processor (IDCP), which uses JPEG,including JPEG-baseline and lossless JPEG, with au-tomatic bit-rate control function. The IDCP adjuststhe compression parameter Q-scale dynamically inreal-time in order to transmit data to a ground stationwithin fixed data rate constantly [53]. Similarly onSolar-B (JAXA2006), an ASIC chip does the 12bitJPEG-baseline and 12bit DPCM compression [54].

5.2. DWT-based compression systems

H-compress, CCSDS-IDC, JPEG2000, and ICERused in the Mars exploration rovers, are all DWT-basedcompression techniques. Although CCSDS-IDC hasnot yet been flown in space, several implementationsof it are in process, including software in C and JAVA,and an ASIC is being developed at the University ofIdaho’s CAMBR [24]. Besides this, PLEIADES-HR

(CNES2010) [92] will fly a compression unit madeof ASICs, using the DWT transform followed by abit plane encoder. This section will mainly introducethe JPEG2000 and ICER compression systems, whichhave already been used in space missions.

5.2.1. JPEG2000 compression systemJPEG2000, the most recent DWT-based image com-

pression standard, can operate at higher compressionratios without generating the blocky and blurry artefactsof the DCT based JPEG-baseline. An image compres-sion payload, GEZGIN, on the Turkish satellite BilSAT-1 (built by SSTL) has used JPEG2000. This standardalgorithm is also used on board IMS-1 (ISRO2008) [93]with a compression ratio of 3.4. The GEZGIN-2 pay-load for RASAT (Turkey2009) and the Parallel Process-ing Unit (PPU) for X-SAT (Singapore2009) are bothgoing to use JPEG2000.

GEZGIN is a digital signal processing card, whichcompresses images from the cameras in real time inJPEG2000 format. GEZGIN meets a 6.5 s constrainton real-time image compression by exploiting the par-allelism among image processing units and assigningcompute intensive tasks to dedicated hardware, as fol-lows: (a) wavelet transformation which consists of 5/3coefficient integer filtering and signal decomposition, isimplemented on a SRAM-based Xilinx Virtex-E FPGAXCV300E and (b) entropy coding and formattingare implemented on a general purpose DSP, namelyTMS320C6701 of TI. With this distributed process-ing, GEZGIN attains a high throughput and maintainsreal-time operation [55,56].

GEZGIN-2, which is currently being developed, isan implementation of the full processing path of theJPEG2000 algorithm in a single SRAM-based FPGA,Xilinx Virtex2-1000. GEZGIN-2 enlarges the flexibilityand efficiency of image compression by including newadjustment parameters like input image size, processingtile size, etc. and by adopting the well-known rate distor-tion optimization facility of the JPEG2000 algorithm tothe real-time processing requirements [57,58]. A blockdiagram of GEZGIN-2 is given in Fig. 6. The imageprocessing FPGA accommodates two banks of externalmemory for temporary storage of the image data dur-ing acquisition and processing. The FPGA’s configura-tion resides in two redundant flash ROM units and isloaded from either one of them on power-up. The con-tent of one of the ROMs could be changed in-orbit whilethe other one remains intact as a back-up. GEZGIN-2 is equipped with high-speed data links complyingwith the ESA SpaceWire standard [59], offering imageand data transfer at a rate of 100Mbps. Two redundant

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 997

Red

Blue

B/W

Green

NearIR

MultispectralImagers

Monochrome Imager

LVDS

LVDS

LVDS

LVDS

LVDS

JPEG 2000 Compressionand Image Preprocessing

(SRAM Based FPGA)

TilingMemory(SRAM)

EBCOTMemory(SRAM)

RSAIC

AESIC

SSDRs

SSDRs

CANNode

CANBUS OBS

CommunicationController

(FLASH FPGA

GOLGE

InterfaceController(Anti-fuse

FPGA)

Space Wire

Space Wire

Space Wire

Space Wire

..

Fig. 6. Block diagram of GEZGIN-2 [58].

SpaceWire links are available. All data links use LVDSat the physical layer for reliable data transfer. A FlashROM based FPGA controls the data-handling among allprocessing units, and also implements the SpaceWirenodes. The command/control interface of GEZGIN-2is a dual redundant CAN bus, managed by a dedicatedCAN controller. The encryption/decryption features areimplemented on a separate board called GOLGE, whichis mounted on GEZGIN-2 as a daughter-board, henceoptional.

The PPU of X-SAT is an onboard technology demon-stration payload for image selection, classification andcompression. The PPU hardware is comprised of 20SA1110 StrongARM microprocessors interconnectedvia two central FPGAs, as shown in Fig. 7. The PPUperforms image compression by using a content-drivenversion of JPEG2000 compression, which is based onunsupervised segmentation. Besides image compres-sion, identified applications also include fire and oildetection [60,61].

5.2.2. ICER compression system on Mars explorationrovers

The Mars exploration rovers “Spirit” and“Opportunity” (NASA2003) have sent back an exten-sive amount of images. Most of the images were com-

Microcontrollerwith CAN

Microcontrollerwith CAN

Flash Flash Flash Flash Flash Flash

PN8

PN7

PN6

PN5

PN4

PN3 PN2 PN1 PN20 PN19 PN18

PN17

PN16

PN15

PN14

PN13PN12PN11PN9 PN10

FPGA1Master

FPGA2Slave

RAM-DiskInterface 1

RAM-DiskInterface 2

Fig. 7. Block diagram of PPU for X-SAT [61].

pressed with the ICER image compression software,while the remaining images that were compressed madeuse of modified low-complexity lossless compression(LOCO) software.

998 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

0

1

2

3

4

5

6

7

-1995

BTCPredict.DCTDWT

1996-1998 1999-2001 2002-2005 2006-

Fig. 8. Theoretical basis vs. time for the compression systems inTable 1.

ICER is a wavelet-based image compressor that fea-tures progressive compression. Following the wavelettransform, ICER compresses a simple binary represen-tation of the transformed image, achieving progressivecompression by successively encoding groups of bits,starting with groups containing highly significant bitsand working toward groups containing less significantbits. The entropy coder is using a lesser known tech-nique called “interleaved entropy coding”. The softwareimplementation has particularly low complexity. Mean-while, given perfect probability-of-zero estimates, botharithmetic coding and interleaved entropy coding cancompress stochastic bit sequences to within 1% of op-timal [62–64]. ICER also incorporates a sophisticatederror-containment scheme to limit the effects of dataloss. The basic idea is to produce the compressed bitstream in separate pieces or segments that can be de-coded independently. ICER is implemented in software,using the VxWorks operating system running on a 20-MHz RAD6000 processor [62].

6. Discussion

In this section we first analyse the algorithms usedin the compression systems described above. Then wediscuss the implementation approaches employed onboard, as well as development trends and other relatedissues.

6.1. Theoretical basis of on-board compression systems

According to the launch year the reviewed space mis-sions in Table 1 are separated into five groups, based onfive time slots, as shown in Fig. 8, where the usage ofthe main types of compression algorithms is plotted as abar graph. As it can be seen, prediction and DCT-basedcompression techniques dominate almost every timeslot. However, in recent years, usage of DWT compres-

sion is increasing, for its fantastic performance result-ing in low-bit-rate compression compared with DCT-based techniques. The primary reason for DCT goingout of stage is the very annoying block artificial effectsintroduced by the block-by-block processing method.Prediction-based techniques are still very popular in re-cent years, for it is the most effective way to achievelossless data compression, which is still a strict require-ment for some payload data.

The reviewed systems are all focused on 2-D imagecompression. MS images are mostly processed band-by-band, which is called intra-band compression. Al-ternatively, images are transformed into another colourspace before intra-band compression. Spectral redun-dancy, which is specific for MS satellite images, canbe removed via inter-band spectral correlation. Thistopic although actively investigated in the literature hasnot yet been applied to satellite on-board compression[65–69].

Hyperspectral (HS) satellite images, which can havemore than 100 spectral bands, require very high-speeddata processing. For example, the HS instrument onboard EO-1 runs at over 500Mbps. This makes HSimage compression an even more challenging task. Anumber of algorithms for HS image compression havebeen developed such as, prediction based [70–73], Vec-tor Quantization based [74–75], hybrid techniques [76],etc.

6.2. Implementation approaches

The implementation approaches to the reviewedcompression systems come into three different cate-gories: software, hardware, or combined software-and-hardware. Software compression systems are executedon general-purpose central processing units (CPUs) orDSP processors. Transputers are classified as general-purpose CPUs as well in this paper. The hardwareimplementations are based on ASICs or FPGAs. Low-cost small satellites, such as BISAT-1 and Beijing-1,have opted for DSP processors and/or FPGA platformsand main-stream compression techniques (JPEG2000and JPEG).

According to the statistics, as shown in Fig. 9, ASICscomprise more than 40% of the implementations of theon-board compression systems in Table 1. The mainreasons for that are the much higher throughput and thehigh-speed processing capabilities of ASICs, comparedwith CPUs or DSP processors. Furthermore, ASICsprovide the possibility for implementation of real-timeimage processing on-board. Customers are inclined toadopt ASICs, if they have already flown successfully

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 999

OBC29%

DSP17%

ASIC43%

FPGA11%

Fig. 9. Implementation approaches to compression.

0

1

2

3

4

5

6OBCDSPASICFPGA

-1995 1996-1998 1999-2001 2002-2005 2006-

Fig. 10. Implementation approaches vs. time.

in space and are easy to integrate into the payload. Forexample, NASA-USES and CNES-ICM ASICs are em-ployed in many missions.

The usage of implementation approaches in the re-viewed compression systems is represented graphicallyin Fig. 10, according to five time slots. ASICs dom-inate four time slots. However, implementations us-ing SRAM-based FPGA are becoming very popularin recent years as they are cheaper and easier to de-velop than ASICs, and in addition are re-programmable.Re-programmability, or reconfiguration, has the advan-tage that the data processing subsystem can be repro-grammed from a ground-based control centre to accountfor mission changes and/or improved algorithms overthe lifetime of the mission.

SRAM-based FPGA are susceptible to single eventupsets (SEUs), which are induced by high-energy par-ticles in the harsh environment of space. This problemis addressed through the use of radiation-tolerant andradiation-hard technologies as well as SEU mitigationtechniques, e.g. triple module redundancy (TMR) [77].The radiation hardened Xilinx FPGA XQR4062XL inHPC-I on board FedSat, described in Section 4.3, is

the first demonstration of hardware reconfiguration inspace [78]. This development was followed by spaceapplications of radiation-tolerant FPGAs, such as boththe lander and the rover vehicles in the Mars Explo-ration Rover mission and the OPTUS C1 communica-tions satellite. The Xilinx Virtex FPGA XCV300E onboard BilSAT-1, although not of radiation tolerant type,still operates in LEO [56]. Successful on-board uses ofSRAM-based FPGAs are steps towards accepting re-configurable logic devices as a flight-proven technologyby the space engineering community.

6.3. Development trends

Here we discuss development trends and some im-portant issues related to the design of image compres-sion systems and satellite imaging payloads.

First of all, error resilience of image compression al-gorithms is especially important for space applications.Fault-tolerant algorithms are required, which can pre-vent or limit the effect of errors occurring during com-pression or during transmission of the compressed datato ground. Through special error resilience algorithmicdesign, ground stations could get maximum valuableinformation out of corrupted compressed files. The de-mand for this functionality has been recognized, forexample, an error-containment scheme has been devel-oped as part of ICER on Mars exploration rovers. Thetiling scheme of JPEG2000 employed on GEZGIN-1and GEZGIN-2, limits the error propagation to one tile.The independent segments scheme of CCSDS-LDC andCCSDS-IDC, is a similar concept aimed at constrainingthe effect of errors to one segment.

Selective image compression is becoming an activetopic whereby classification is used as a pre-processingstage for decision-making regarding the compressionprocess. In this case the classification results are usedto determine where the areas of interest are in the im-age, so that these areas are treated differently duringcompression. Examples of such systems are the adap-tive JPEG-LS of SAICS on FedSAT, the region of non-interest (RONI) scheme of JPEG2000 on GEZGIN-2,and the so-called content-driven version of JPEG2000based on unsupervised segmentation on X-SAT.

On-board storage is a very important issue in imag-ing payloads. The mass memory for raw or processedimage data is normally composed of high-speed, highly-reliable solid state recorders (SSRs) based on SDRAMand FLASH devices. The solid state data recorder(SSDR) on board DMC missions is capable of storing8Gbits of data. But SSRs can have a higher cost perbit and lower storage density (bits/in2), compared with

1000 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

Tiling

Storage/Downlink

CamerasLossless image

compression

Lossy imagecompression

Radiometric Calibration

Registration

Change Detection

Classification

ImageCompression

SpectralDecorrelation Encryption

Tiling

Storage/Downlink

CamerasLossless imagecompression

Lossy imagecompression

Radiometric Calibration

Registration

Change Detection

Classification

ImageCompression

SpectralDecorrelation Encryption

Fig. 11. Block diagram of the proposed on-board image compression system.

hard disc drives (HDDs), when very high capacitiesare required. HDD is very promising for future mis-sions, although there are several obstacles to overcome[79]. The DMC mission, has actually used a HDD fortechnology demonstration on board Beijing-1.

The CAN bus is a good choice as an interface be-tween the main computer and the image processing pay-load, for its simplicity and high reliability. CAN hasbeen employed in SSTL satellites, Smart-1, GEZGIN-2 of RASAT, etc. Recently, the high-speed SpaceWireinterface, used on GEZGIN-2, is attracting a lot of in-terest. SpaceWire is very promising, as it provides low-latency communication, throughput scalability and faulttolerance capability [59].

Several researchers have recently successfully down-loaded unauthorized satellite data directly from passing-by satellites [80–82], which has illustrated the need foron-board encryption in imaging payloads. Encryptionhas been adopted on SPOT-5 and GEZGIN-2.

7. An on-board image compression system forfuture disaster monitoring missions in LEO

In this section, an on-board real-time image compres-sion system for future disaster monitoring missions isproposed.

Future satellite missions will be capable of carry-ing out more intelligent on-board processing such asimage classification and change detection, which areimportant for disaster monitoring applications. Imageclassification is primarily used for cloud detection andclassification. Clouds are a very common problem inoptical EO, being effectively unwanted “applicationnoise”. The performance of JPEG can be improved inconjunction with cloud editing [85]. Change detectionanalysis which requires two or more images acquiredover time, can help to improve the transmission band-width by sending to ground only the part of the image,which contains the identified changes, referred to as

change image [86]. The incorporation of a fine-grainedtiling scheme in the compression process, has provedto increase the resilience of image compression algo-rithms to single-bit errors [83,84].

General purpose 2-D image compression meth-ods, although easier to adopt, are not the most suit-able techniques for compression of remotely sensedimages. Two methods are used to achieve spectraldecorrelation—inter-band prediction or Kahunen–Loeve transform (KLT). KLT, which is considered theoptimum method to spectrally decorrelate MS data[65], is a topic of active research at the moment. Anexperiment and results, regarding the effect of numberof bands on decorrelation efficiency, are presented in[65]. The conclusion is that KLT cannot so effectivelydecorrelate images with a number of bands lower thanfour. The KLT transform will have a role to play onboard future disaster monitoring missions, as they areexpected to have an increased number of MS bands.

Fig. 11 shows a functional block diagram of the pro-posed system for compression of PAN and MS images.After coming out of the cameras, the image data is pro-cessed serially, tile-by-tile, first undergoing some pre-processing, which improves compression or enables thesystems to make intelligent decisions about the com-pression process. The envisaged pre-processing tasksare radiometric calibration, registration, change detec-tion and image classification. Radiometric calibrationrefers to the conversion of the raw data to take into ac-count the sensor radiance quality. As the parameters ofthe radiometric calibration are changing with time, thisfunctional block should be made reconfigurable.

The image compression block consists of a spectraldecorrelation unit and a 2-D image compression unit,which provides both lossless and lossy compression. Anencryption block is included to encrypt the compressedimage data. The processed data could be stored in on-board mass memory or sent to the downlink module,from where it is downloaded to the ground station. All

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 1001

Table 3Compression Results of JPEG-LS and JPEG2000 on PAN test imageswithout and with radiometric calibration, corresponding to PAN andPAN-R.

Compression ratio JPEG-LS JPEG2000 (lossless)

PAN PAN-R PAN PAN-R

DC000055hp 4.056 4.212 3.976 3.900DC000138hp 5.576 5.663 5.4530 5.7729DC000189hp 3.482 3.539 3.3989 3.3989DC000219hp 4.336 4.539 4.5504 4.7156DC000231hp 2.967 3.010 2.8396 2.8315DC000285hp 3.553 3.692 3.4986 3.5175DC000286hp 3.152 3.240 3.137 3.094DC000288hp 4.053 4.191 4.0126 4.0137DC000305hp 3.003 3.019 2.9359 2.9099AVE 3.7976 3.9006 3.7558 3.7949

of these processing units can be bypassed, so that rawimages are kept, which is required in some cases. It isenvisioned that a radiation-tolerant SRAM-based FPGAwill be used as the central processing component, forits advantages expounded in Section 6.2.

Two different radiometric calibration techniques arecompared in [94]. An autonomous band registrationtechnique featuring robustness has been proposed in[95]. For lossy image compression we have selectedthe CCSDS-IDC scheme, which is specifically designedfor space applications, as it can achieve similar perfor-mance to JPEG2000 while featuring lower complexityof JPEG2000 [17]. For lossless image compression, wehave selected the JPEG-LS algorithm, as it achievescomparable performance to the CALIC algorithm [87],which is a state-of-the-art lossless compression methodin terrestrial applications, but has much lower complex-ity [13]. What’s more, an efficient lossless image com-pression system has been proposed and implementedusing an FPGA in [96,97]. KLT is selected to spectrallydecorrelate MS images in our system.

In the rest of this section we include experimentalresults with SSTL satellite images to illustrate the per-formance of the selected algorithms. A PAN test imageset was composed of nine images with different fea-tures from the DMC satellite Beijing-1. Table 3 showsthe compression results for JPEG-LS and JPEG2000on these images without and with radiometric calibra-tion (correspondingly PAN and PAN-R). The JPEG-LSsoftware used is the SPMG/JPEG-LS ImplementationV.2.1 1999, while JPEG2000 software is Kakadu V5.1.Radiometric calibration parameters for Beijing-1, pro-vided by SSTL, have been used. The results confirmedfirst of all that JPEG-LS outperforms JPEG2000 with

Table 4Compression Results of JPEG-LS and KLT+JPEG-LS on MS testimages.

Compression ratio JPEG-LS KLT+JPEG-LS

389-sea 2.93 2.86389-bigcloud 7.64 6.24389-farmland 2.13 2.21389-mountains 2.15 2.21397-smallcloud 2.25 2.16c7p-farmland 2.46 2.46c7p-mountains 2.63 2.63c7p-sea 4.21 3.92c7p-urban 2.23 2.27AVE 3.181 2.996

respect to lossless compression. In addition they provedthat radiometric calibration could improve the compres-sion ratio by around 26%, which is a significant gain.

Also a second test image set consisting of nine 3-bandMS images captured from UK-DMC and NigeriaSAT-1 was composed. These images have already under-gone radiometric calibration and inter-band registration.Table 4 shows the compression results of JPEG-LS andKLT+JPEG-LS on these images. As it can be seen KLThas not achieved an improvement in images which fea-ture cloud or water (sea). This has confirmed that aclassification to identify cloud, water, and other landtype areas, should be done before compression. Whatis more, the compression of classified cloud, or waterarea should be treated differently. The somewhat lim-ited improvement of KLT on other land types is proba-bly due to the number of bands being lower than fouras explained above.

8. Conclusions

This paper has reviewed image compression systemson board current and emerging space missions. Statisti-cal analysis and developing trends are presented. Com-pression algorithms, implementations and other criticalissues are discussed. A new architecture of an on-boardimage compression system for future disaster moni-toring missions is proposed. The architecture featuresintelligent pre-processing and spectral decorrelation toaccount for future needs of disaster monitoring fromspace.

Acknowledgements

The authors gratefully acknowledge the provisionof satellite images from SSTL and DMC InternationalImaging for the experimental results in this paper.

1002 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

Thanks are also due David Cooke and Charles Coxfrom SSTL.

References

[1] J. Laycock, V. Van der Zel, N. Morris, G. Park, W. Levett,High resolution optical imaging with the low cost TopSat smallsatellite, in: Proceedings of the 2004 International AstronauticalCongress, IAC-04-IAA.4.11.4, Vancouver, Canada, October4–8, 2004.

[2] P. Brooks, TopSat—high resolution imaging from a smallsatellite, in: Proceedings of the Third IAA Symposium on SmallSatellites for Earth Observation, Berlin, Germany, April 2–6,2001, pp. 319–322.

[3] S. Cawley, TOPSAT: low-cost high resolution imagery fromspace, in: Proceedings of the Fourth IAA Symposium on SmallSatellites for Earth Observation, Berlin, Germany, April 7–11,2003.

[4] M. Boland, L. Boland, A. da Silva Curiel, A. Phipps, M.N.Sweeting, Vista—A constellation for real time regional imaging,in: Proceedings of the 53rd International Astronautical Congressof the International Astronautical Federation (IAF), Houston,TX; United States; October 10–19, 2002.

[5] A. da Silva Curiel, M. Wazni, L. Boland, P. Davies, S. Eves,W. Sun, M. Sweeting, Rapid response high resolution imagingfrom space, (ESA SP-571), in: Proceedings of the FourthSymposium—Small Satellites Systems & Services, ESA/CNES,La Rochelle, France, September 20–24, 2004.

[6] C. Lambert-Nebout, G. Moury, A survey of on-board imagecompression for CNES space missions, in: Proceedings ofthe 1999 IEEE International Geoscience and Remote SensingSymposium, 1999, pp. 2032–2034.

[7] Theory of Data Compression, Last retrieved in June 2006 from〈http://www.data-compression.com/theory.shtml〉.

[8] N.S. Jayant, P. Noll, Digital Goding of Waveforms, Principlesand Applications to Speech and Video, Prentice-Hall,Englewood Cliffs, NJ, 1984.

[9] M. Rabbani, P.W. Jones, Digital Image CompressionTechniques, SPIE Press, Bellingham, WA, 1991.

[10] B.V. Brower, D. Couwenhoven, B. Gandhi, C. Smith, ADPCMfor advanced LANDSAT downlink applications, in: ConferenceRecord of The 27th Asilomar Conference on Signals, Systemsand Computers, vol. 2, November 1–3, 1993, pp. 1338–1341.

[11] CCSDS, Lossless data compression, recommendation for spacedata system standards, CCSDS 121.0-B-1, May, 1997.

[12] W.B. Pennebaker, J.L. Mitchell, JPEG Still Image DataCompression Standard, Chapman & Hall, New York, 1993.

[13] M.J. Weinberger, G. Seroussi, G. Sapiro, The LOCO-I losslessimage compression algorithm: principles and standardizationinto JPEG-LS, IEEE Transactions on Image Processing 9 (8)(2000) 1309–1324.

[14] Information Technology—JPEG2000 Image Coding System,Final Committee Draft Version 1.0, ISO/IEC JTC l/SC 29/WG1 N1646R, March, 2000.

[15] J.M. Shapiro, Embedded image coding using zero trees ofwavelet coefficients, IEEE Transactions on Signal Processing41 (1993) 3445–3462.

[16] A. Said, W.A. Pearlman, A new fast and efficient imagecodec based on set partitioning in hierarchical trees, IEEETransactions on Circuits and Systems for Video Technology 6(1996) 243–250.

[17] CCSDS, 2005. Image data compression, recommendation forspace data system standards, CCSDS 122.0-B-1, November,2005.

[18] E. Delp, O. Mitchell, Image compression using block truncationcoding, IEEE Transactions on Communications 27 (9) (1979)1335–1342 (legacy, pre-1988).

[19] P.-S. Yeh, G.A. Moury, P. Armbruster, CCSDS data compressionrecommendation: development and status, in: Proceedings ofthe SPIE—The International Society for Optical Engineering,vol. 4790, 2002, pp. 302–313.

[20] P.S. Yeh. Implementation of CCSDS lossless data compressionfor space and data archival applications, in: Proceedings of theSpace Operations Conference, 2002.

[21] P.-S. Yeh, J. Venbrux, A high performance image datacompression technique for space applications, in: NASA EarthScience Technology Conference, Maryland, USA, 2003.

[22] J. Serra-Sagrista, C. Fernandez-Cordoba, et al., Lossy codingtechniques for high resolution images, in: Proceedings of SPIE,vol. 5238, 2003, pp. 276–287.

[23] J. Serra-Sagrista, F. Auli-Llinas, et al., Review of CCSDS-ILDC and JPEG2000 coding techniques for remote sensing, in:Proceedings of the SPIE, vol. 5573, 2004, pp. 250–261.

[24] P.-S. Yeh, P. Armbruster, A. Kiely, B. Masschelein, G. Moury,C. Schaefer, C. Thiebaut, The new CCSDS image compressionrecommendation, in: IEEE Conference on Aerospace, March5–12, 2005, pp. 1–8.

[25] A. Brewer, Implementation of image compression on PoSAT-1, International Journal of Small Satellite Engineering 1 (1)(1995).

[26] Y. Zheng, G. Ke, M. Sweeting, Tsinghua micro/nanosatelliteresearch and its application, in: Proceedings of the 13thAIAA/USU Conference on Small Satellites, SSC99-IX-3,Logan, UT, August 23–26, 1999.

[27] M. Othman, TiungSAT-1: from Inceptionto Inaugration,Astronautic Technology (M), Sdn Bhd, Kuala Lumpur, 2001.

[28] B.V. Brower, M.A. Cosgrove, D.H. Lewis, G.R. VanLare, T.J.Fisher, D.M. Charneski, An advanced space-qualified downlinkimage compression ASIC for commercial remote sensingapplications, in: Proceedings of SPIE on Applications of DigitalImage Processing XXIII, vol. 41115, 2000, pp. 311–319.

[29] B.V. Brower, A. Lan, M.A. Cosgrove, D.H. Lewis, G.R.VanLare, An enhanced space-qualified downlink imagecompression ASIC for commercial remote sensing applications,in: Proceedings of SPIE on Applications of Digital ImageProcessing XXV, vol. 4790, 2002, pp. 314–320.

[30] H.J. Kramer, WorldView-1, Last retrieved in June 2006 from〈http://directory.eoportal.org/pres_WorldView1.html〉.

[31] R.F. Rice, Adaptive variable-length coding for efficientcompression of spacecraft television data, IEEE Transactionson Communications (1971) 889–897.

[32] R.F. Rice, Some practical universal noiseless coding techniques,JPL Publication 79–22, Jet Propulsion Laboratory, Pasadena,California, March, 1979.

[33] R.F. Rice, Some practical universal noiseless codingtechniques—part II, JPL Publication 83–17, Jet PropulsionLaboratory, Pasadena, California, March, 1983.

[34] R.F. Rice. Some practical universal noiseless codingtechniques—part III, JPL Publication 91–3, Pasadena,California, November, 1991.

[35] Cambr.uidaho.edu. Universal source encoding for science data.Last Retrieved in February 2006, from 〈http://www.cambr.uidaho.edu/chips/comp.asp〉.

G. Yu et al. / Acta Astronautica 64 (2009) 988–1005 1003

[36] M.L. Decker, R. Rex Kay, N.G. Rackley, Multispectral thermalimager (MTI) satellite imaging operations and performance,in: AIAA/USU Conference on Small Satellites, SSC01-V-2,Logan, UT, August 13–16, 2001.

[37] P.R. Christensen, B.M. Jakosky, H.H. Kieffer, M.C. Malin,H.Y. McSween, K. Nealson, G.L. Mehall, S.H. Silverman, S.Ferry, M. Caplinger, M. Ravine, The thermal emission imagingsystem (THEMIS) for the Mars 2001 odyssey mission, SpaceScience Reviews 110 (1–2) (2004) 85.

[38] T. Smith, J. Kessler, Earth Orbiter 1: Wideband AdvancedRecorder and Processor (WARP), NASA no. 19990024890〈http://eo1.gsfc.nasa.gov/miscPages/GOMAC.PDF〉.

[39] T. Smith, Wideband Advanced Recorder and Processor (WARP)Validation Report, NASA Goddard Space Flight Center,March 19, 2002 〈http://eo1.gsfc.nasa.gov/new/validationReport/Technology/Documents/Reports/WARP.pdf〉.

[40] PGCU (PICARD Gestion Charge Utile), Last retrieved in June2006 from 〈http://smsc.cnes.fr/PICARD/GP_instruments.htm〉.

[41] H.J. Kramer, Presentation of STEREO (Solar-TerrestrialRelations Observatory), Last Retrieved in February 2006, from〈http://directory.eoportal.org/pres_STEREOSolarTerrestrialRelationsObservatory.html〉.

[42] A.S. Dawood, S.J. Visser, J.A. Williams, Reconfigurable FPGAsfor real time image processing in space, in: Proceedings ofthe 14th International Conference on Digital Signal Processing,Institute of Electrical and Electronics Engineers, New York.

[43] A.S. Dawood, J.A. Williams, S.J. Visser, On-board satelliteimage compression using reconfigurable FPGAs, in: IEEEInternational Conference on Field-Programmable Technology(FPT). Proceedings (Cat. No. 02EX603), 2002, pp. 306–310.

[44] S.J. Visser, A.S. Dawood, J.A. Williams, FPGA based satelliteadaptive image compression system, Journal of AerospaceEngineering 16 (3) (2003) 129–137.

[45] C. Lambert-Nebout, C. Latry, G.A. Moury, C. Parisot, M.Antonini, M. Barlaud, On-board optical image compression forfuture high-resolution remote sensing systems, in: Proceedingsof SPIE on Applications of Digital Image Processing XXIII,vol. 4115, December 2000, pp. 332–346.

[46] G.A. Moury, C. Latry, In-orbit commissioning of SPOT5 imagecompression function, in: Proceedings of the SPIE on EarthObserving Systems VIII, vol. 5151, 2003, pp. 540–551.

[47] B.N. Handy, L.W. Acton, et al., The transition region andcoronal explorer, Solar Physics 187 (2) (1999) 229–260.

[48] D. Berghmans, J.F. Hochedez, et al., SWAP onboard PROBA2, a new EUV imager for solar monitoring, Advances in SpaceResearch, vol. 38, 2006, pp. 1807–1811.

[49] H.J. Kramer, MicroLabSat, Last retrieved in June 2006 from〈http://events.eoportal.org/pres_MicroLabSat.html〉.

[50] G.W. Milne, A. Schoonwinkel, et al., SUNSAT—Launch andfirst six months’ orbital performance, in: Proceedings of the13th Annual AIAA/Utah State University Conference on SmallSatellites, Logan, UT, United States, August 23–26, 1999.

[51] H.J. Kramer, Presentation of TEAMSAT/YES as part ofMaqSat-H, Last retrieved in June 2006 from 〈http://directory.eoportal.org/pres_TEAMSATYESaspartofMaqSatH.html〉.

[52] J. Bermyn, PROBA—project for on-board autonomy, Air &Space Europe 2(1) (2000) 70–76.

[53] H. Hihara, et al., High speed image data compression processorfor ALOS, 2002-n-30, 23rd ISTS, Shimane, May, 2002.

[54] SOLAR-B Project, Observation control and science data flow,Last retrieved in June 2006 from 〈http://solar.nro.nao.ac.jp/solar-b/sot_e/DataFlow.html〉.

[55] N. Ismailoglu, O. Benderli, et al., A real time imageprocessing subsystem: GEZGIN, Breakthrough Technologies—The Foundation of the Future, 2002.

[56] G. Yuksel, O. Belce, H. Urhan, BILSAT-1: first Earthobservation satellite of Turkey—operations and lessons learned,in: Proceedings of Second International Conference on RecentAdvances in Space Technologies, RAST 2005, June 9–11, 2005,pp. 846–851.

[57] N. Ismailoglu, O. Benderli, et al., GEZGIN-2: an advancedimage processing subsystem for Earth-observing smallsatellites, in: Proceedings of Second International Conferenceon Recent Advances in Space Technologies RAST 2005, June9–11, 2005, pp. 605–610.

[58] N. Ismailoglu, O. Benderli, S. Yesil, R. Sever, B. Okcan,O. Sengul, R. Oktem, GEZGIN & GEZGIN-2: adaptive real-time image processing subsystems for earth observing smallsatellites, in: Adaptive Hardware and Systems, 2006, AHS 2006,First NASA/ESA Conference, June 15–18, 2006, pp. 351–358.

[59] ECSS-E-50-12 A (ISO/IEC), SpaceWire Standard.[60] T. Trenschel, T. Bretschneider, G. Leedham, Using JPEG2000

on-board mini-satellites for image-driven compression, in:Proceedings of the IEEE International Geoscience and RemoteSensing Symposium, vol. 3, 2003, pp. 2033–2035.

[61] T. Bretschneider, B. Ramesh, V. Gupta, I. McLoughlin,Low-cost space-borne processing on a reconfigurable parallelarchitecture, in: Proceedings of the International Conference onEngineering of Reconfigurable Systems and Algorithms, 2004,pp. 93–99.

[62] A. Kiely, M. Klimesh, The ICER progressive wavelet imagecompressor, The Interplanetary Network Progress Report42-155, July–September 2003, Jet Propulsion Laboratory,Pasadena, California, pp. 1–46, November 15, 2003 〈http://ipnpr.jpl.nasa.gov/progressreport/42-155/155J.pdf〉.

[63] A. Kiely, M. Klimesh, Preliminary image compression resultsfrom the Mars exploration rovers, IPN Progress Report, vol.42–156, February 15, 2004, pp. 1–8.

[64] A. Kiely, M. Klimesh, J. Maki, ICER on Mars: wavelet-based image compression for the Mars exploration rovers, INDTechnology and Science News, Issue 15, June, 2002, pp. 15–19.

[65] J.A. Saghri, A.G. Tescher, J.T. Reagan, Practical transformcoding of multispectral imagery, IEEE Signal ProcessingMagazine 12 (1) (1995) 32–43.

[66] C. Lambert-Nebout, et al., Fixed data rate wavelet compressorfor multispectral satellite systems, in: A.G. Tescher (Ed.),Proceedings of SPIE, Applications of Digital Image ProcessingXXV, vol. 4790, November 2002, pp. 346–335.

[67] P. Luigi Dragotti, G. Poggi, A.R.P. Ragozini, Compression ofmultispectral images by three-dimensional SPIHT algorithm,IEEE Transactions on Geoscience and Remote Sensing 38 (1)(2000) 416–428.

[68] J. Lee, Optimized quadtree for Karhunen–Loeve transformin multispectral image coding, IEEE Transactions on ImageProcessing 8 (4) (1999) 453–461.

[69] S. Benierbah, M. Khamadja, Compression of colour images byinter-band compensated prediction, IEE Proceedings on Vision,Image and Signal Processing 153 (2) (2006) 237–243.

[70] R.E. Roger, M.C. Cavenor, Lossless compression of AVIRISimages, IEEE Transactions on Image Processing 5 (5) (1996)713–719.

[71] B. Aiazzi, P. Alba, L. Alparone, S. Baronti, Losslesscompression of multi/hyperspectral imagery based on a 3-Dfuzzy prediction, IEEE Transactions on Geoscience and RemoteSensing 37 (5) (1999) 2287–2294.

1004 G. Yu et al. / Acta Astronautica 64 (2009) 988–1005

[72] J. Mielikainen, P. Toivanen, Clustered DPCM for the losslesscompression of hyperspectral images, IEEE Transactions onGeoscience and Remote Sensing 41 (12) (2003) 2943–2946.

[73] H. Wang, S.D. Babacan, K. Sayood, Lossless hyperspectralimage compression using context-based conditional averages,in: Proceedings of the Data Compression Conference, DCC2005, March 29–31, 2005, pp. 418–426.

[74] M.J. Ryan, J.F. Arnold, The lossless compression ofAVIRIS images by vector quantization, IEEE Transactions onGeoscience and Remote Sensing 35 (3) (1997) 546–550.

[75] S.-E. Qian, Hyperspectral data compression using a fast vectorquantization algorithm, IEEE Transactions on Geoscience andRemote Sensing 42 (8) (2004) 1791–1798.

[76] G.P. Abousleman, M.W. Marcellin, B.R. Hunt, Compressionof hyperspectral imagery using the 3-D DCT and hybridDPCM/DCT, IEEE Transactions on Geoscience and RemoteSensing 33 (1) (1995) 26–34.

[77] Xilinx TMRTool—The First Triple Module RedundancyDevelopment Tool for re-configurable FPGAs, Last retrievedin June 2006 from 〈http://www.xilinx.com/esp/mil_aero/collateral/tmrtool_sellsheet_wr.pdf〉.

[78] XILINX chips enable world’s first “on-the-fly” reconfigurablesatellite, Xilinx Press Release #0317 〈http://www.xilinx.com/prs_rls/design_win/0317satellite.htm〉.

[79] J.M. Coupe, Data handling challenges for remote sensingsystems, in: Proceedings of the Aerospace Conference, 2004,IEEE, vol. 4, 6–13 March 2004, pp. 2263–2268.

[80] K. Sweet, The increasing threat to satellite communications,Online Journal of Space Communication 6, 2003.

[81] Critical Infrastructure Protection, Commercial satellite securityshould be more fully addressed, US Government AccountabilityOffice Report, GAO-02-781, August, 2002.

[82] K. Poulsen, Satellites at Risk of Hacks, Security Focus, October,2002 〈http://www.securityfocus.com/news/942〉.

[83] T. Vladimirova, A. Steffens, Compression of multispectralimages on-board observation satellites, in: Proceedings of theInternational Conference on Space, Ecology, Safety (SES’05),vol. 1, Varna, Bulgaria, TU, Sofia, June 10–13, 2005, pp.105–110.

[84] T. Vladimirova, M. Meerman, A. Curiel, On-board compressionof multispectral images for small satellites, in: IEEEInternational Conference on Geoscience and Remote SensingSymposium, IGARSS 2006, 2006, pp. 3533–3536.

[85] P. Hou, M. Petrou, C.I. Underwood, A. Hojjatoleslami,Improving JPEG performance in conjunction with cloudediting for remote sensing applications, IEEE Transactions onGeoscience and Remote Sensing 38 (1) (2000) 515–524.

[86] S. Yuhaniz, T. Vladimirova, M.N. Sweeting, Embeddedintelligent imaging on-board small satellites, in: Srikanthan, etal. (Eds.), Lecture Notes in Computer Science, ACSAC 2005,vol. 3740, Springer, Berlin, Heidelberg, 2005, pp. 90–103.

[87] X. Wu, N.D. Memon, Context-based, adaptive, lossless imagecoding, IEEE Transactions on Communications 45 (4) (1997)437–444.

[88] H. Sun, X. Chen, The payload data management system forthe Chang’E-1, in: 59th International Astronautical Congress,Glasgow, Scotland, 2008.

[89] C. Peanvijarnpong, D. Dowreang, N. Aphicholati, W.Kodchabudthada, M. Piboon, Thailand Earth ObservationSystem (THEOS): A new dimension of Thailand remotesensing, in: Proceedings of 26th Asian Conference on RemoteSensing, ACRS, Hanoi, Vietnam, 2005.

[90] M. Krishnaswamy, CartoSAT-2: A dedicated satellite forcartographic application, in: 59th International AstronauticalCongress, Glasgow, Scotland, 2008.

[91] G. Tyc, J. Steyn, N. Hannaford, J. Gebbie, B. Stocker, A.Baker, M. Oxfort, RapidEye—a cost-effective Earth observationconstellation, in: 59th International Astonautical Congress,Glasgow, Scotland, October 2008.

[92] C. Thiebaut, X. Delaunay, C. Latry, G. Moury, CNES studiesfor on-board compression of high-resolution satellite images,in: Satellite Data Compression, Communication, and ProcessingIV, San Diego, CA, USA, 2008.

[93] R. Srinivasan, J.R. Subramanian, M. Pitchaimani, S.K.Shivakumar, TWSAT—Mission Operations Plan, in: 59thInternational Astronautical Federation, Glasgow, Scotland,2008.

[94] G. Yu, T. Vladimirova, M. Sweeting, A new automaticon-board multispectral image compression system for LeoEarth observation satellites, in: The 15th IEEE InternationalConference on Digital Signal Processing, 2007, pp. 395–398.

[95] G. Yu, T. Vladimirova, M. Sweeting, Autonomous bandregistration for on-board applications, in: IEEE InternationalConference on Signal Processing and Communications, ICSPC2007, 2007, pp. 1327–1330.

[96] G. Yu, T. Vladimirova, X. Wu, M.N. Sweeting, A new high-level reconfigurable lossless image compression system forspace applications, in: NASA/ ESA Conference on AdaptiveHardware and Systems, AHS-2008, ESTEC, Noordwijk, TheNetherlands, 2008.

[97] G. Yu, T. Vladimirova, M.N. Sweeting, An efficient on-board lossless compression design for remote sensing imagedata, in: IEEE International Geoscience and Remote SensingSymposium, IGARSS 2008, Boston, MA, USA, 2008.

Guoxia Yu received the B.S. degreein Electronics and System from theNankai University, Tianjin, China, in2001 and the M.S. degree in ElectronicEngineering from Tsinghua University,Beijing, China, in 2004.He is currently a Ph.D. research studentwith Surrey Space Centre, Universityof Surrey, Guildford, UK. His currentresearch interests include image com-pression, remote sensing, digital signalprocessing, and FPGA design.

Tanya Vladimirova received a M.Sc. in Applied Mathematics fromthe Technical University of Sofia, Bulgaria, a M.E. in ComputerSystems Engineering and a Ph.D. in VLSI Design from the St. Pe-tersburg Electro-Technical University (LETI), Russia.She is currently a Reader at the Department of Electronic Engi-neering of the University of Surrey and leads the VLSI Design andEmbedded Systems research group at the Surrey Space Centre. Herresearch interests are in the areas of system-on-a-chip, FPGA de-sign, image processing, intelligent embedded systems, and wirelesssensor networks.

Martin N. Sweeting received a Ph.D. degree in Electronic Engi-neering from the University of Surrey, Guildford, United Kingdom.He formed a spin-off University company (SSTL—Surrey Satellite

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Technology Ltd.) in 1985. SSTL is now the world’s leading mi-crosatellite company. He is currently the Chief Executive Officer ofSSTL. He is also the Director of the academic Surrey Space Centre.In 1995, Sir Martin was awarded the OBE in HM Queen’s Birthday

Honours and the Royal Academy of Engineering Silver Medal—bothin recognition of his pioneering work in small satellites. In 1996,he was elected a Fellow of the Royal Academy of Engineering.