New Simple Decomposition Technique for Polarimetric SAR Images
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1 Korean Journal of Remote Sensing, Vol.26, No.1, 2010, pp.1~7 New Simple Decomposition Technique for Polarimetric SAR Images Kyung-Yup Lee and Yisok Oh Department of Electronic Information and Communication Engineering, Hongik University, Seoul, Korea Abstract : This paper proposes a new decomposition technique for polarimetric synthetic aperture radar (SAR) images. This new decomposition technique is based on the degree of polarization (DoP) and co-polarized phase-difference (CPD) of the measured polarimetric backscattering coefficients. This decomposition technique is compared with the existing three- and four-component decomposition techniques with the ALOS PALSAR full polarimetric L-band data acquired in It is shown that the new decomposition technique is better or comparable to the existing techniques for the study areas such as sea, bare soil, forest, and urban area. Key Words : decomposition, SAR image classification technique, degree of polarization, copolarized phase difference. yisokoh@hongik.ac.kr 1
2 Korean Journal of Remote Sensing, Vol.26, No.1, 2010 (a) (b) (c) Fig. 1. Three scattering-mechanism model: (a) surface scatter, (b) double-bounce scatter (c) volume scatter. T C M P t = P s + P d + P v (1) P t P s P d P v 2
3 New Simple Decomposition Technique for Polarimetric SAR Images Q s2 + U s2 + V s2 DoP = (1) I s Q s U s V s Ï s <ΩE hω2 > + s <ΩE vω2 Ï I > s s F s <ΩE hω2 > _ s Q <ΩE vω2 > = s = (2) < 2Re(E s s* U s he v )> Ó V s < 2Im(E s s* he v )> Ó E s h E s v s i ÏE e _ jk 0 r h ÏS = hh S hv ÏE h s r i (3) ÓE v ÓS vh S vv Ó E v F c F hh F vv < Im(S hh S* vv ) > < Re(S hh S* vv ) > f c = tan _ 1 (4) I s P v P t P v Dop a Dop a Dop a P v P t a P v = P t (1 _ a) (5) a a P v P v P v P sd P sd = P s + P d = P t a (6) Ωf cω Ωf cω P d P sd Ωf cω ΩCPDΩ P d = P t a (7) 180 P sd Ωf cω P sd Ωf cω 180 P s = P t a _ ΩCPDΩ (8) 180 3
4 Korean Journal of Remote Sensing, Vol.26, No.1, 2010 P v P d P s P t ÏR [S s ] = H 0 (9) Ó 0 R V Ïb 2 0 b [C s ] = f s, f s = ΩR VΩ 2 R H 0 0 0, b = (10) R V b 0 1 Ó R H R V ÏR [S d ] = GH R TH 0 (11) Ó 0 _ R GV R TV Ïa 2 0 _ a [C d ] = f d, f s =ΩR GV R TVΩ 2 R GH R TH 0 0 0, a = (12) _ R GV R TV a 0 1 Ó R GH R GV R TH R TV Ï 1 0 1/3 [C v ] = f v 0 2/3 0 (13) 1/3 0 1 Ó f s f d f v a b P = f s (1 + ΩbΩ 2 ) + f d (1 + ΩaΩ 2 ) + 8f v /3 = P s + P d + P v (14) S HH S * HV S VV S * HV S HH S * HV S VV S * HV [S LH ] = 1 Ï 1 j 1 and [S RH ] = 1 Ï _ j _ (15) 2 2 Ó j _ 1 Ó j _ 1 4
5 New Simple Decomposition Technique for Polarimetric SAR Images (a) (a) (b) Fig. 2. Application of the new algorithm to the PALSAR image of Choon-chun area; (a) Aerial photo and (b) DoP-CPD decomposition with Ps (blue), Pd (red), Pv (green). (b) Fig. 3. SAR image decomposition with (a) 3-component and (b) 4-component decomposition techniques with Ps (blue), Pd (red), Pv (green). Table 1. Average Backscatter Values Results of The New DOP-CPD Decomposition Technique CPD DoP PtDoP-CPD DoP-CPD Decom. Decom. [db] [db] [deg.] DoP [db] Pv Ps Pd River Bare soil Forest Village Table 2. Average Backscatter Values Results of The Existing Decomposition Techniques for The Same Areas 3-com. decom. 3-com. [db]4-com. decom. [db] decom. [db] 4-com. decom. [db] Pv Pv Ps Pd Ps Pv Pd Pv Ps Pd Ps Pd Pc Pc River Bare soil Forest Village
6 Korean Journal of Remote Sensing, Vol.26, No.1, 2010 Boerner, W.-M, Recent advances in extra-wideband polarimetry, interferometry and polarimetric interferometry in synthetic aperture remote sensing and its applications, IEE Proceedings - Radar, Sonar and Navigation, 150(3): Cloude S. R., and E. Pottier, A Review of Target Decomposition Theorems in Radar Polarimetry, IEEE Trans. Geosci. Remote Sensing, 34(2): Elies P., B. Le Jeune, P. Olivard, J. Cariou, and J. Lotrian, The application of de-polarization analysis to polarimetric characterization and classification of metallic and dielectric samples, J. Phys. D: Appl. Phys
7 New Simple Decomposition Technique for Polarimetric SAR Images Freeman A., and S. L. Durden, A Three- Component Scattering Model for Polarimetric SAR Data, IEEE Trans. Geosci. Remote Sensong, 36(3). Lee, J. S., en, Ainsworth, T. L., Kelly, J. P., and Lopez-Martinez, C., Evaluation and Bias Removal of Multilook Effect on Entropy/ Alpha/Anisotropy in Polarimetric SAR Decomposition, IEEE Trans. Geosci. Remote Sensong, 46(10): Lee, J. S., and E. Pottier, 2009, Polarimeric Radar Imaging; From Basics to Applications, CRC Press. Oh, Y., Lee, K. Y., Jang, G., New Unsupervised Classification Technique for Polarimetric SAR Images, Korean Journal of Remote Sensing, 25(3): Rio V. S., J. M. Mosquera, M. V. Isasa, and M. E. Lorenzo, Statistics of the degree of polarization, IEEE Trans. Antennas Propag., 54(7): Rosenqvist, A., Shimada, M., Ito, N., and Watanabe, M., ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment, IEEE Trans. Geosci. Remote Sensong, 45(11): Sinclair G., The transmission and reception of elliptically polarized waves Proc. IRE, 1950, pp Ulaby F. T., and C. Elachi, Radar Polarimetry for Geoscience Applications, Artech House Remote Sensing Library. Ulaby F. T., K. Sarabandi, and A. Nashashibi, Statistical properties of the Mueller matrix of distributed targets, IEE Proceedings-F, 139(2): Van zyl, Unsupervised classification of scattering behavior using radar polarimetry data, IEEE Trans. Geosci. Remote Sensing., 27(1): Yamaguchi, Y., Moriyama, T., Ishido, M., and Yamada, H., Four-component scattering model for polarimetric SAR image decomposition, IEEE Trans. Geosci. Remote Sensing, 43(8):
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