Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity

Fan Qing-hui; Lu Hong-xi; Bao Zheng; Xiao Chun-bao

doi:10.11999/JEIT141468

Volume 37 Issue 8

Aug. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(8): 1821-1827

Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468

Citation:

Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468

Citation:

PDF( 5183 KB)

Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity

doi: 10.11999/JEIT141468 cstr: 32379.14.JEIT141468

Received Date: 2014-11-24
Rev Recd Date: 2015-04-24
Publish Date: 2015-08-19

Abstract

Abstract

Target decomposition is an important tool to realize target classification, detection and recognition applications with Polarimetric SAR (PolSAR). However, the traditional method with priority of volume scattering component extraction seriously performs overestimation in the volume scattering energy or underestimation in the dihedral scattering energy. In this paper, by introducing polarimetric similarity measure, data-driven model- matching for basic scattering mechanism is proposed. On this basis, the priority of scattering mechanisms energy extraction is determined with the similarity measure. Based on the non-negative constraint of energy, all the orders of residual matrix are reextracted for the final energy contribution of the dihedral scattering, volume scattering, and surface scattering mechanism. The processing results of real data and their comparison with the optical image results show that the proposal is better than traditional methods for the accurate extracttion of the basic scattering characteristics in the targets region.
- Polarimetric SAR (PolSAR),
- Target decomposition,
- Polarization similarity,
- Optimal model matching

FullText(HTML)

References(17)

References

Boerner W M, Yan W L, Xi A Q, et al.. Basic Concepts of Radar Polarimetry[M]. Netherlands: Springer, 1992: 155-245.

Boerner W M. Basics of SAR Polarimetry I[R]. Chicago, IL: 2007.

Mott H. Remote Sensing with Polarimetric Radar[M]. New York: Wiley-IEEE Press, 2007: 3-19.

Cloude S R. Polarisation: Applications in Remote Sensing[M]. Oxford: Oxford University Press, 2009: 4-103.

Lee J S and Pottier E. Polarimetric Radar Imaging From Basics to Applications[M]. United States: CRC Press, 2009: 5-53.

Zebker H A and van Zyl J J. Imaging radar polarimetry: a review[J]. Proceedings of the IEEE, 1991, 79(11): 1583-1606.

Chen Q, Kuang G Y, Li J, et al.. Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1817-1825.

Frery A C, Cintra R J, and Nascimento A. Entropy-based statistical analysis of PolSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3733-3743.

Kajimoto M and Susaki J. Urban-area extraction from polarimetric SAR images using polarization orientation angle[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2): 337-341.

Zhang P, Li M, Wu Y, et al.. Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model[J]. Pattern Recognition, 2013, 46(4): 1-16.

Ballester-Berman J D and Lopez-Sanchez J M. Applying the Freeman-Durden decomposition concept to polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(1): 466-479.

Freeman A and Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973.

Yamaguchi Y, Sato A, Sato R, et al.. Four-component scattering power decomposition with rotation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2251-2258.

Yamada H, Komaya R, Yamaguchi Y, et al.. Scattering component decomposition for POL-InSAR dataset and its applications[C]. Geoscience and Remote Sensing Symposium, Cape Town, 2009: V-154-V-157.

Van Zyl J J, Arii M, and Kim Y. Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3452-3459.

Cloude S R and Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.

Singh G, Yamaguchi Y, and Park S E. General four- component scattering power decomposition with unitary transformation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 3014-3022.

Relative Articles

Supplements(0)

Cited By

Proportional views