Application of Multi-frequency SAR Images forKnowledge Acquisition

V.Battsengel1*, D.Amarsaikhan2 and A.Munkh-Erdene2

1School of Geography and Geology, National University of MongoliaIkh Surguuliin gudamj-6, Ulaanbaatar-46, Mongolia

Tel/Fax: 976-11-322822

2Institute of Informatics, Mongolian Academy of Sciencesav.Enkhtaivan-54B, Ulaanbaatar-51, Mongolia

Tel: 976-11-453660, Fax: 976-11-458090

Abstract: The aim of this research is to apply TerraSAR X-band,Envisat C-band and Palsar L-band synthetic aperture radar (SAR)images for a knowledge acquisition process. For the study,backscattering properties of different natural and man-madeobjects of urban environment are analyzed on the basis ofstatistics of signatures of the selected classes. After theknowledge acquisition, for the acquired knowledge representation,a rule-based approach is proposed. Overall, the researchdemonstrated that the multi-frequency radar images can beeffectively used for the knowledge acquisition as well as for theanalysis of different land cover types.

Keywords: Knowledge acquisition, SAR, Microwave wavelengths,Backscattering

1. Introduction

A knowledge-based system (KBS) is part of artificial intelligencethat emulates problem-solving processes of human experts in aspecific domain. In many cases, a KBS consists of a userinterface, knowledge base and inference engine. The knowledge basecontains the rules and facts derived from human experts, while theinference engine performs the logical deduction reasoning andknowledge synthesis, and generates solutions to a given problem.The KBSs are especially useful when solution of problems mainlyrelies on the empirical knowledge of human experts, when multiplesolutions are in consideration [11]. These systems are not new inRS image analysis and in many cases have been used for automaticimage understanding and interpretation [5]. Different types ofthese systems have been and are being developed depending upon thesolutions of the given problems and the structures of knowledgerepresentation [4].

In general, the most commonly quoted problems for development ofthe KBSs are the unavailability of the good experts and knowledgeengineers as well as the difficulties with the rule extractionprocess. In other words, it is a problem of knowledge acquisition[10]. As the main task of a KBS is to provide solutions to aproblem in a specific domain, utilizing the knowledge andexpertise embodied in it, the necessary knowledge should be mainlyextracted from human experts through a knowledge acquisitionprocess. Also, such knowledge may be taken from other sources suchas literature in a given problem domain or other field andempirical data sets [2]. For the optical RS data sets, a set ofknowledge can be acquired from the reflective and emissiveproperties of objects or classes of interest, whereas for themicrowave images such knowledge could be acquired from thebackscatter properties natural and man-made features.

At microwave wavelengths, three types of scattering such assurface scattering, volume scattering, and double bouncescattering occur, and most radar image analyses are based on them.If the surface is homogeneous, then surface scattering will occurand it can be either specular or diffuse, or intermediatedepending on the wavelength and surface roughness. If the surfaceis dielectrically inhomogeneous, then volume scattering whereradar penetrates the surface and the return is due to scatteringfrom the underlying materials, will occur. Double bouncescattering occurs, when the right angles are formed betweennatural and artificial objects. In addition, radar interpretationsare very much environment or site specific and relative to thefrequency as well as polarization, incidence angle, surfaceproperties and the effects of water and soil moisture. The tonalvariations on the radar images also depend on the changes of theboundary condition between specular and diffuse scattering[1,5,7].

The aim of this study is to analyze the backscatteringcharacteristics of different urban land cover types used for aknowledge acquisition process and describe an appropriatetechnique for the acquired knowledge representation. For thispurpose, different land cover classes have been selected fromTerraSAR X-band, Envisat C-band and Palsar L-band radar images ofurban area in Mongolia, and analyzed in relation to the surfaceand system parameters. For the final analysis, the grey levelvalues of a group of contextually dependent pixels selected from

different parts of the images have been used and compared on thebasis of the mean values (M) and standard deviation (SD).

2. Test site and data sources

As a test site, Ulaanbaatar, the capital city of Mongolia has beenselected. The selected part of the city is characterized by suchmain classes as building area, ger area (Mongolian traditionaldwelling), forest, grass, soil with high moisture, soil with lowmoisture, open area and water. The building area includesbuildings of different sizes, while ger area includes mainly gerssurrounded by fences. The forest class consists of different typesof tall and short trees located along the Tuul River. The grassmainly includes grassland area, but there are some bush and shorttrees, too. The soil with high moisture is located along the areawhich previously was a river valley. The soil with low moisture ismainly distributed along the northern range of the Tuul River. Thewater class represents the Tuul River located in the southern partof Ulaanbaatar. Figure 1 shows an Envisat image of the test site,and some examples of its land cover.

Figure 1. 2011 Envisat image of the selected part of Ulaanbaatar.1-building area, 2-ger area,

3-forest, 4-grass, 5-soil with high moisture, 6-soil with lowmoisture, 7-open area and 8-water.

In the present study, the data consisted of TerraSAR X-band HHpolarization image of March 2008 with a spatial resolution of 1m,Envisat C-band HH polarization image of March 2010 with a spatialresolution of 25m, and Palsar L-band HH polarization image of May2006 with a spatial resolution of 25m. Also, as additional groundtruth information a topographic map of 2000, scale 1:5000,multichannel SPOT XS image of 2009 as well as soil and vegetationmaps of scale 1:100.000 were available.

3. Knowledge acquisition

In general, knowledge acquisition is used for an initialintelligent guess of the spectral values of selected classes andit is important for selection of the reliable features as well asfor definition of reliable spatial and spectral thresholds. In thepresent study, knowledge acquisition has been conducted based onthe theory of backscattering mechanisms of each class availablewithin the selected image frame.

Initially, from different parts of the SAR images, polygonsrepresenting the selected land cover types have been selectedusing local knowledge. Then, the polygons were transformed intoprimary signatures (ERDAS 1999) of the representative classes. Asthe images have high and very high spatial resolutions, the finalsignatures included different numbers of pixels. The signatureselected sites from TerraSAR and Palsar images are shown infigures 2 and 3, and the mean values and SDs for the chosentraining samples selected from the multi-frequency SAR images areshown in table 1.

Figure 2. The signature selected sites in TerraSAR image ofUlaanbaatar. 1-building area, 2-ger area, 3-forest, 4-grass, 5-

soil with high moisture, 6-soil with low moisture, 7-open area and8-water.

As seen from table 1, the building area has the highest meanvalues in X and C bands and the second highest mean value in Lband. It is statistically separable from most classes, but in Xband its backscatter values are highly scattered. Compared to thebuilding area, a ger area might easily have some overlaps withother classes specifically in X band, unless accurate spectralthreshold values are selected. In general, the backscatter fromurban areas should contain information about street alignment,building size, density, roofing material, its orientation as wellas vegetation and soil, thus resulting in all kinds of scattering.Roads and buildings in urban areas can reflect a larger componentof radiation, if they are aligned at right angles to the incidentradiation. Here, the intersection of a road and a building tendsto act as a corner reflector. The amount of backscatter is verysensitive to street alignment. The areas of streets and buildingsaligned at right angles to the incident radiation will have asaturated very bright appearance and non-aligned areas will have abright/dark appearance in the resulting image. Volume and surface

scattering will also play an important role in the response frommany of the urban features [4,7]. Using Rayleigh’s criterion ofsurface roughness, for the TerraSAR X (3.1cm) band data, theboundary between the diffuse and specular reflection can bedetermined as being between .38cm and 1.45cm, while for theEnvisat C (5.6cm) band data it could be in between 0.75 cm and0.79 cm, depending on the incident angles. Many urban surfaceshave variations that are just greater or less than these values.

Figure 3. The signature selected sites in Palsar image ofUlaanbaatar. 1-building area, 2-ger area,

3-forest, 4-grass, 5-soil with high moisture, 6-soil with lowmoisture, 7-open area and 8-water.

To form the signatures of ger area and building classes, thepolygons representing these classes were selected from morehomogeneous parts of the images. However, the investigation of theindividual pixels indicated that those objects aligned at rightangles gave extremely high backscatter values and they reachedtheir maximum in all X, C and L bands. In addition, as seen fromtable 1, at X-band frequency ger area almost overlaps with soilwith high moisture, but at C and L-band frequencies the twoclasses are completely separable. This is most probably due to the

fact that soil moisture is more saturated at the upper 3cm(penetrating capability of X-band) of the soil causing highbackscatter return at X-band frequency, while at the depth of 6cm-23cm (penetrating capability of C and L-bands) the soil surfacecondition is more homogeneous causing moderate backscatter return.

In case of forest, at X and C band frequencies, canopy scatteringand attenuation will be caused primarily by leaves and needles,because the wavelength is too short to penetrate into the forestlayer. However, at L-band frequency the wavelength will penetrateto the forest canopy and will cause volume scattering to bederived from multiple-path reflections among twigs, branches,trunks and ground. As seen from table 1, the forest has moderatebackscatter return and it has some statistical overlaps with thegrass in L band. The grass will act as mixtures of small bush,grass and soil and the backscatter will depend on the volume ofeither of them [1]. Although plant geometry, density and watercontent are the main factors influencing the backscatter comingfrom the vegetation cover, ground truth information revealed thatthe contribution of vegetation is not very significant during thistime of the year. Comparing the mean values of forest and grass,one can observe that they are more separable at short radar bandsdue to stronger volume scattering in the forest area at thosefrequencies.

There are two soil classes having different backscatter values. Asseen, the soil with low moisture forms more compact signatures,whereas the soil with high moisture forms scattered clusters. Thebackscatter of soil depends on the surface roughness, texture,existing surface patterns, moisture content, as well as wavelengthand incident angle. The presence of water strongly affects themicrowave emissivity and reflectivity of a soil layer. At lowmoisture levels there is a low increase in the dielectricconstant. Above a critical amount, the dielectric constant risesrapidly. This increase occurs when moisture begins to operate in afree space and the capacity of a soil to hold and retain moistureis directly related to the texture and structure of the soil[1,4]. As it can be seen from table 1, soil with low moisture haslower values in comparison with all other classes. This indicateslower backscatter intensities caused by specular reflection due tolack of some surface features, low roughness properties and lowdielectric constant of the dry soil. In contrast to the soil withlow moisture, wet soil gives high backscatter return compared to

the most of the classes because of the soil moisture content andincreased dielectric constant.

Table 1. The mean backscatter values of the selected land coverclasses and

their variations in the TerraSAR and Quickbird bands.

No Classes TerraSAR Envisat PalsarM SD M SD M SD

1 Building area 175.2 71.3 231.6 33.2 201.6 41.22 Ger area 132.2 64.4 190.9 30.3 247.3 14.43 Forest 90.9 42.1 122.2 19.9 168.8 39.94 Grass 71.4 28.3 95.3 18.9 151.5 43.5

5 Soil with high moisture 147.3 50.2 134.3 23.3 115.4 38.1

6 Soil with low moisture 63.2 21.1 42.0 20.4 23.5 22.1

7 Open area 57.1 17.5 82.5 19.7 19.8 16.18 Water 89.1 29.1 29.2 13.1 37.8 23.5

In most cases, open area will behave as a specular reflector, butin short wavelengths and at some specific conditions wheresufficient surface roughness is observed, it will have somecomponents of diffuse scattering. As seen from table 1, at SARwavelengths the open area has very low average backscatter returncompared to almost all other classes and it has a high statisticaloverlap with soil with low moisture in X and L bands.Specifically, in L-band, its backscatter values are lower than thewater, which means that the selected site is totally dominated bya specular reflection and there is very little return signaltoward the radar antenna.

Generally, in an urban environment, most of the available waterresources will have specular reflection and should appear verydark on images for all incident angles except 0. To obtain somebackscatter from a water surface, it must by some mechanism, bemade rough. The principal mechanism for the roughening the watersurface is the generation of waves and in reality the waves can begenerated by strong wind. However, in the given case, there iscontextual influence of grass, green vegetation and small bush(from both sides of the river) due to which there is increasedbackscatter return (especially in X-band).

4. THE PROPOSED knowledge representation

In general, urban areas are very diverse and create complexsystems that have very explicit characteristics. These make theurban system possible to distinguish from all other systems. Forexample, it is necessary to consider the physical aspects such assize, structural aspects such as composition, economic aspectssuch as cost, and environmental aspects such as relation with itsenvironment as well as the aspects connected to its operation(i.e., utilization by people, interaction with concerned users).In everyday life, an organization or a municipality manages anurban system and their work consists of arranging, planning andmanaging these urban systems [9]. Therefore, development of theurban knowledge base requires a complex approach that considersall urban aspects.

For development of a proper KBS used for the automaticinterpretation of such multi-frequency SAR images, the aboveknowledge about backscattering properties of different natural andartificial objects of urban environment can be represented in amost efficient way, for example, using a rule-based approach. Itis one of the most commonly used knowledge representationtechnique, in which different rules mainly containing theconstraints on expert-defined variables, spatial objects, externalprograms and other spatial models are constructed and used for thehypothesis evaluation [3]. Thus, different parameters to berequired might be formulated as a set of ‘IF THEN’ rules and theactual image processing can be done on the basis of a forwardchaining principle [8]. Moreover, the rule-based inference basedon the forward chaining principle can be mapped into neuralnetwork architecture. For this purpose, at first, data attributesshould be assigned to input nodes, final hypotheses should beassigned to output nodes and the hypotheses must be assigned tohidden units. Then, the initial domain rules determine how theattributes and hypotheses are linked and how the links areweighted.

5. Conclusions

The aim of this study was to conduct knowledge acquisition throughthe analysis of the backscattering properties of the urban landcover features in Mongolia using TerraSAR X-band, Envisat C-bandand Palsar L-band SAR images. In case of equal calibration, it was

possible to compare the backscattering properties of the separateradar bands. However, as the calibration of each SAR image wasdifferent, a comparison was made among the classes within thebands themselves. Within the framework of the study, anappropriate technique, that is a rule-based approach for theacquired knowledge representation was proposed. Overall, the studydemonstrated that the multi-frequency radar images could besuccessfully used for the knowledge acquisition as well as for theinvestigation of different urban land surface features.

6. References

1. Amarsaikhan, D., and Ganzorig, M., 1999, Interpretation andcomparison of AirSAR quad-polarised radar images. Proceedings of the20th Asian Conference on RS, Hong Kong, China, 22-26 November 1999,pp.695-700.

2. Amarsaikhan, D., Ganzorig, M. and Battsengel, V., 2005,Knowledge Acquisition in C-band and L-band Radar Frequencies,CD-ROM Proceedings of the Asian Conference on RS, Hanoi, Vietnam, pp.DTP4-1_1-4.

3. Amarsaikhan, D.,Ganzorig, M., Ache, P. and Blotevogel, H., 2007,The Integrated Use of Optical and InSAR Data for Urban LandCover Mapping, International Journal of Remote Sensing, 28, pp.1161-1171.

4. Amarsaikhan, D., Blotevogel, H.H., van Genderen, J.L. andGanzorig, M., 2009, Knowledge acquisition on urban land coverfeatures using TerraSAR and Quickbird images, CD-ROM Proceedings ofthe Asian Conference on RS, Beijing, China.

5. Daniels, A.E., 2006, Incorporating domain knowledge and spatialrelationships into land cover classifications: a rule-basedapproach, International Journal of Remote Sensing, 27, pp.2949–2975.

6. ERDAS, 1999, Field guide, Fifth Edition, ERDAS, Inc. Atlanta,Georgia.

7. Manual of Remote Sensing, 1999, 3rd Edition, Volume 3, AmericanSociety of Photogrammetry.

8. Onsi, H.M., 2003, Designing a rule-based classifier usingsyntactical approach, International Journal of Remote Sensing, 24, pp.637–647.

9. Quintero, A. and Pierre, S., 2002, A knowledge-based approachfor managing urban infrastructures, Knowledge-Based Systems,15,pp.449–463.

10. Wagner, W.P., Otto, J. and Chung, Q.B., 2002, Knowledgeacquisition for expert systems in accounting and financialproblem domains, Knowledge-Based Systems,15, pp.439–447.

11. Xu, L.D., Liang, N. and Gao, Q., 2001, An integratedknowledge-based system for grasslands ecosytems, Knowledge-BasedSystems,14, pp.271-280.

Scientific Research Publishing

Advances in Remote Sensing

ISSN Print: 2169-267X ISSN Online: 2169-2688 Website: http://www.scirp.org/journal/ars

Paper ID: 2630041 Paper Title: KNOWLEDGE ACQUISITION ON URBAN LAND COVER TYPES USINGX, C AND L-BAND RADAR IMAGES Author(s): V Battsengel