基于半正定约束的极化相似度最优模型匹配目标分解

范庆辉; 卢红喜; 保铮; 肖春宝

doi:10.11999/JEIT141468

基于半正定约束的极化相似度最优模型匹配目标分解

doi: 10.11999/JEIT141468

基金项目:

国家自然科学基金(61271024, 61201292, 61201283)，新世纪优秀人才支持计划(NCET-09-0630)，全国优秀博士学位论文作者专项资金(FANEDD-201156)，省部级基金和中央高校基本科研业务费

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出版历程
- 收稿日期: 2014-11-24
- 修回日期: 2015-04-24
- 刊出日期: 2015-08-19

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

摘要

摘要: 目标分解是实现极化合成孔径雷达目标分类、检测与识别应用的重要手段。传统方法由于优先对体散射分量进行提取，其体散射能量的高估或二面角散射能量的低估现象较为严重。该文通过引入极化相似度量，基于数据驱动自适应地对基本散射机制的最优匹配模型进行选择。在此基础上，根据极化相似度量确定基本散射机制散射能量提取的优先顺序，并以各阶次剩余矩阵能量非负为约束，最终确定面散射、二面角散射、体散射这3种基本散射机制的能量贡献值。实测数据处理结果及其与光学图像的对比结果表明，该文方法获取的极化目标分解结果优于传统方法，能够准确地提取目标区域的基本散射特征。
- 极化合成孔径雷达 /
- 目标分解 /
- 极化相似度 /
- 最优模型匹配
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

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参考文献(17)

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.

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