CLASSIFICATION OF EARTH TERRAIN COVERS USING THE MODIFIED FOUR- COMPONENT SCATTERING POWER DECOMPOSITION,

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1 CLASSIFICATION OF EARTH TERRAIN COVERS USING THE MODIFIED FOUR- COMPONENT SCATTERING POWER DECOMPOSITION, Boularbah Souissi (1), Mounira Ouarzeddine (1),, Aichouche Belhadj-Aissa (1) USTHB, F.E.I, BP N 3 El Alia Bab Ezzouar, Alger, Algérie, m.ouarzeddine@lycos.com ABSTRACT Polarimetric classification is one of the most significant applications of polarimetric synthetic aperture radar (PolSAR) in remote sensing. During the last decade, several research tasks revealed the contribution of polarimetric data in soil types and land cover classification. They contributed thus to a better comprehension of the scattering mechanisms and target identification. In this paper, we aim to identify and analyze earth terrain covers using the more recent approach which is the modified four-component scattering power decomposition derived from the coherency matrix. It is the extension of the Freeman- Durden decomposition which is based on three components model: surface, double bounce and volume scattering. This new method added an asymmetric component represented by the helix scatterer as a forth component for describing man-made targets in the urban area scattering and it has been developed to deal with the reflection and nonreflecton symmetry. The first point of this paper is to show the modification and the theoretical expansion for the covariance matrix of the nonsymmetry case. Secondly, four images have been generated corresponding to the four components of the new approach represented by: surface, double bounce, volume and helix scatterers. The results show the useful of using this approach for general scattering case for man-made (nonreflection symmetric case) and natural targets (reflection symmetric case) classification. The test site is the Oberpffafenhoffen in Munich. The SAR images used are acquired using airborne P-band fully polarimetric radar. Key words: polarimetry, scattering mechanisms, symmetry, classification, decomposition 1. INTRODUCTION Fully understanding and retrieving information from polarimetric SAR data have become a key issue for SAR remote sensing and its broad applications. Terrain and land-use classifications are one of the most important applications of PolSAR. Earlier classification algorithms of PolSAR images were based on their statistical characteristics. Recently, the classification uses the inherent characteristics of physical scattering mechanisms to separate different types of scatterers in the imaged terrain by using various techniques referred as target decomposition theorems [1]. These techniques were first outlined by Chandrasekhar for light scattering by small anisotropic particles and later applied to polarized MW by huynen [] who had a subtle impact on the steady advancement of polarimetry and gave an impetus to further development, which continues today. The objective for any decomposition is to manipulate the scattering matrix elements in order to obtain more descriptive target parameters by means of an analysis of polarization matrices that describe the target. These techniques have been developed that involves the fit of a combination of certain number of simple scattering mechanisms to polarimetric SAR observations. an excellent review has already been published on the subject of the TD that can be used to gain a better understanding of polarimetric scattering mechanism measurements can be found in [][3]. In this paper, we present an analysis on the land cover types as forests, agriculture fields, and urban areas. In the same studied image, there are many corners reflectors for radar calibration. These reflectors are used also used to demonstrate the effectiveness of the decomposition which is the modified four component model method. As known, speckle noise [4] is the main problem in SAR image interpretation and will affect the accuracy of the classification. The data has been filtered from this noise before any application by using the Lee filter [5]. The study site is located closer to the German Space Center (DLR) and about 5 Km from southwest of Munich, Germany. It is known as Oberpfafenhoffen. Fig 1a. an 1b. represented by the Pauli and the optical give a detail about this site. Proc. of 4th Int. Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry PolInSAR 009, 6 30 January 009, Frascati, Italy (ESA SP-668, April 009)

2 (a) (b) Figure 1. a) Pauli image, with HH-VV, HV, and HH+VV, for the three composite colors (red, green, and blue, respectively),.b) Optical image (by Google earth). POLARIMETRIC BASED ON THE MODIFIED FOUR COMPONENT MODEL DECOMPOSITION Four component scattering model is proposed [6] to extend the three-component decomposition method introduced by Freeman and Durden [7] dealing with the reflection symmetry condition which generally appears in the natural medium and disappears in the complex urban areas. The forth added component is the helix scattering term which has the asymmetric reflection condition which takes the effect of the relations s s 0, and s s 0 into account. This extended model accommodates the general geometric scattering structures. The flow chart shown in Fig resumes the two approaches. S S The covariance or the coherency matrix of the fourcomponent decomposition model has the following form [6]: T T T C f s f c surface T helix f d double f v volume (1) Where f s, f d, f v and f c are the surface, double bounce, volume and helix scattering parameters. After making several algebraic simplifications, the model for the total scattered power gives four decomposed powers p s, p d, pv and p c [6]. The contributions of each of the four scattering mechanisms to the total power are shown for each pixel in the Fig. 3, and 4., and S Reflection symmetry SS, SS General case SS SS 0 C S 0 SS 0 S 0 S S S 0 C S S S S S S S S S S S S S S S SS SS 0, 0 Three component decomposition Four component decomposition Application : Natural distributed target areas Application: Natural distributed target areas Figure. comparaison between the Freeman-Durden and the Yamaguchi decomposition

3 3. Results and discussion Three images have been extracted which are representing the four scattering targets mentioned in the theoretical analysis and are given by the power:ps, Pd, Pv and Pc. The first three images represented by Ps, Pd, Pv are combined (Fig 4a). These four images give a detailed informations about the kind of the terrain objects depending on the type of the scattering mechanisms. The entropy image represented in Fig 4c, shows the degree of the randomness of the different terrain types. In this figure, the forested area has high values of the entropy because of its volume scattering characteristics. The agriculture fields have an entropy varying from low to medium. For the urban area, the entropy is a mixture of low, medium and high values because of its complex structures. P s image P d image P v image Figure 3. the three resulted image of the decompositiion: surface scattering (Ps), double bounce scattering and volume scattering images Forests Urban area Trihedral calibrator Agriculture fields (a) (b) (c) Figure 4. a) Polarimetric decomposition result in RGB, R(Pd), G(Pv) and B(Ps), b) Image represents the Fourth added component Which is the helix scattering element and c) Entropy image.

4 4. THE FOUR BACKSCATTERING VALUES AND THE ENTROPY Cloud and pottier have used two parameters (the entropy and the alpha angle) to form a two dimentional space containg the caracteristics of eight different classes. The first parametre which is the entropy representes the randomne of the targets, and the alpha angle is used to identify the type of the scattering mechanism as odd bounce, double bounce or dipole scattering. In this decomposition, the alpha angle represents one image containing values from 0 to 90. But in the case of the yamagushi decomposition, four power images are used to caracterize the three different scattering mechanisms as mentioned by the Entropy/Alpha decomposition plus the helix asymetric target. In the figures shown in Fig 5. an analysis of some different land covers is discussed. This ananalysis is based on a combination of the four images and the entropy image for three different areas. We have taken the agriculture, the urban and the forested areas as mentioned in the RGB image shown in Fig 4a as examples for our study analysis. For the first area which is the agriculture fields, we see from Fig 5. that the first graph (5.1a) indicate the dominance of the surface scattering with low and medium entropy. In the urban area, we see that the helix scattering is the most dominant in this area (Fig 5.d). from the profile shown in Fig 6., we see that the helix is more dominante than the Double scattering in the urban area. The forested area represented by Fig 5.3a-3d, have the volume scattering caracteristics (Fig 5.3c) with less contribution of the helix scattering. (1a) (1b) (1c) (1d) (a) (b) (c) (d) (3a) (3b) (3c) (3d) Figure 5. Spectral analysis of the four backscattering values vs the entropy, 1) agriculture fields, ) Urban area and 3) Forested area with (a) P s vs H, (b) P d vs H, (c) P v vs H and (d) P h vs H Figure 6. Histogramms in the urban area a) Helix scattering, b) Double bounce scattering

5 TRIHEDRAL CALIBRATORS the trihedral calibrators as mentioned in the optical image have different backscattering as shown in the entropy and the four yamagushi decomposition components. The odd bounce image shows 5 point targets represented by bright pixels which have big backscattering values of about db. These points have the maximum values because they have the odd bounce scattering characteristics. The two images of the entropy and the helix have five black points because of their very low backscattering values ( 0 for the entropy, and -0dB in the helix image). For the rest of the images namely the double bounce and volume images, the points are not dominant but they have equal values of about 0dB. The profile shown in Fig 7h. gives the right backscattering values for the four decomposed images for one trihedral corner calibrator. (a) (b) (c) (d) (h) (e) Figure 7. a) Entropy image, b) Odd bounce image, c) double bounce image, d) Volume bounce image, e) Helix bounce image and h) spectral graph of one trhidral power for the four bands ( P s, P d, P v and P h ) 5. CONCLUSION In this poster, we have applied the new decomposition approach which is the four component-component scattering model on the L-band polarimeric ESAR image. This approach proposed by Yuki et all. is the extension of the Three-component model proposed by Freeman and Durden which is based on the symmetric conditions such as in the forested areas. The four components are represented by surface, double-bounce, volume and helix scattering. This decomposition accommodates the general geometric scattering structures for both symmetric and non-symmetric cases. The fourth added component which is the helix has the asymmetric characteristics and it appears in complex urban areas and disappears in naturally distributed scattering medium. 6. REFERENCES 1. Cloude, S. R. & E. Pottier. A review of target decomposition theorems in radar polarimetry, IEEE transaction on Geoscience and. Remote Sensing, vol. 34, pp , 199 [] Touzi, R., Boerner, W.M., Lee, J.S., & Lueneburg E., A review of polarimetry in the context of synthetic aperture radar: concepts and

6 information extraction,. Can. J. Remote Sensing, (3), pp , Boerner, W-M.. Basics of SAR polarimetry II. NATO Research and Technology Organisation, J.S. Lee, M. R. Grunes, and G. De Grandi, Polarimetric SAR Speckle Filtering and its Implication for Classification, IEEE Trans. Geosci. Remote Sensing, Vol. 37, (5), pp , Sep Lee, J.S., Grunes, M.R., T.L. Ainsworth, Du, L., Schuler,D.L. & Cloude,S.R. «Unsupervized Classification of Polarimetric SAR Images by Applying Target Decomposition and Complex Wishart Distribution» PIERS 1998, Nantes, France, July 1998, also IEEE TGRS, vol. 37, no.5, pp49-58, Sept Yamaguchi,Y., Moriyama, T., Ishido, M., & H. Yamada. Four-component scattering model for polarimetric SAR image decomposition, IEEE Trans. Geoscience and Remote Sensing, vol. 43, ( 8), pp , Aug Freeman, A. & Durden., S.L. A three component scattering model for polarimetriuc SAR data. IEEE transaction on Geoscience and. Remote Sensing, (3), pp , 1998.