一种基于多极化散射机理的极化SAR图像舰船目标检测方法

文伟; 曹雪菲; 张学峰; 陈渤; 王英华; 刘宏伟

doi:10.11999/JEIT160204

一种基于多极化散射机理的极化SAR图像舰船目标检测方法

doi: 10.11999/JEIT160204

基金项目:

国家杰出青年科学基金(61525105)，国家自然科学基金(61201292, 61322103, 61372132)，全国优秀博士学位论文作者专项资金(FANEDD-201156)，陕西省自然科学基础研究计划(2016JQ- 6048)，航空科学基金(20142081009)和航空电子系统射频综合方针航空科技重点实验室基金联合资助，上海航天科技创新基金(SAST- 2015009)

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出版历程
- 收稿日期: 2016-03-03
- 修回日期: 2016-08-23
- 刊出日期: 2017-01-19

PolSAR Ship Detection Method Based on Multiple Polarimetric Scattering Mechanisms

Funds:

The National Science Fund for Distinguished Young Scholars (61525105), The National Natural Science Foundation of China (61201292, 61322103, 61372132), The Program for New Century Excellent Talents in University (FANEDD-201156), The Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6048), The Aviation Science Fund (20142081009) and Key Laboratory Fund of RF Integrated Laboratory in Avionics System, Shanghai Aerospace Science and, Technology Innovation Fund (SAST2015009)

摘要

摘要: 针对基于单一极化特性增强的极化SAR图像目标检测方法的缺陷，该文将DP(Dirichlet Process)混合隐变量SVM模型(DPLVSVM)应用于极化SAR图像舰船目标检测，提出一种基于多极化散射机理的检测方法。该方法通过联合Dirichlet过程混合与Bayes SVM模型，将信号空间划分成若干局部区域，然后在每一局部区域学习一个独立的极化检测器，并将各局部检测器进行组合实现全局多极化散射机理的目标检测。模型采用非参数化Bayes方法自动确定局部区域数量，在完全Bayes框架下，将局部区域划分及检测器学习进行联合优化，保证了各局部区域样本的可分性。另外，为了降低极化特征冗余，该文进一步提出带特征选择功能的稀疏提升DP混合隐变量SVM模型(SPDPLVSVM)，提高模型的推广能力。该模型由于采用共轭先验分布，因而可以利用Gibbs采样方法进行高效求解。在RADARSAT-2数据上进行的实验验证了所提方法的有效性。
- 极化SAR /
- 目标检测 /
- Dirichlet过程混合模型 /
- Bayes SVM /
- 特征选择
Abstract: Considering the shortcoming of detection method based on polarimetric contrast enhanced with single polarimetric scattering mechanism, a PolSAR detection method based on multiple polarimetric mechanisms called Dirichlet Process mixture of Latent Variable SVM (DPLVSVM) is proposed. By assembling a set of local polarimetric detectors that based on single polarimetric scattering mechanism, a global multiple polarimetric scattering mechanisms detector is obtained. With a fully Bayes treatment, DPLVSVM learns the clustering and the local detectors jointly. Taking the advantage of Bayes nonparametric, DPLVSVM handles the model selection problem flexibly. Further, in order to reduce the redundancy of polarimetric feature and improve the model generalization, a model with feature selection, Sparsity-Promoting Dirichlet Process mixture of Latent Variable SVM (SPDPLVSVM), is proposed. Thanks to the conjugate property, the parameters in both of models can be inferred efficiently via the Gibbs sampler. Finally, the proposed models on RADARSAR-2 dataset is implemented to validate their effectiveness.
- Polarimetric SAR /
- Target detection /
- Dirichlet process mixture model /
- Bayes SVM /
- Feature selection

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参考文献(31)

CRISP D J. The state of the art in ship detection in synthetic aperture radar imagery[R]. Technical Report DSTO-RR-0272, DSTO Information Sciences Laboratory, South Australia, Australia, 2004.

TOUZI R, CHARBONNEAU F, HAWKINS R K, et al. Ship- sea contrast optimization when using polarimetric SARS[C]. IEEE 2001 International Conference on Geoscience and Remote Sensing Symposium, Sydney, Australia, 2001: 426-428.

邢艳肖, 张毅, 李宁, 等. 一种联合特征值信息的全极化SAR图像监督分类方法[J]. 雷达学报, 2016, 5(2): 217-227. doi: 10.12000/JR16019.

XING Yanxiao, ZHANG Yi, LI Ning, et al. Polarimetric SAR image supervised classification method integrating eigenvalues[J]. Journal of Radars, 2016, 5(2): 217-227 doi: 10.12000/JR16019.

ARMANDO Marino. A notch filter for ship detection with polarimetric SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3): 1219-1232. doi: 10.1109/JSTARS.2013.2247741.

YEREMY M, CAMPBELL J W M, MATTAR K, et al. Ocean surveillance with polarimetric SAR[J]. Canadian Journal of Remote Sensing, 2001, 27: 328-344. doi: 10.1080/ 07038992.

NOVAK L M, MICHAEL B S, and MICHELE J C. Studies of target detection algorithms that use polarimetric radar data[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 150-165. doi: 10.1109/7.18677.

NOVAK L M, BURL M C, and IRVING W W. Optimal polarimetric processing for enhanced target detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 234-244. doi: 10.1109/NTC.1991.147989.

YANG Jian, ZHANG Hongji, and YOSHIO Yamaguchi. GOPCE-based approach to ship detection[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(6): 1089-1093. doi: 10.1109/LGRS.2012.2191611.

LEE Jongsen, HOPPEL K W, MANGO S A, et al. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1017-1028. doi: 10.1109/36.312890.

WANG Yinghua and LIU Hongwei. PolSAR ship detection based on superpixel-level scattering mechanism distribution features[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8): 1780-1784. doi: 10.1109/LGRS.2015.2425873.

陈博, 王爽, 焦李成, 等. 利用0-1矩阵分解集成的极化SAR图像分类[J]. 电子与信息学报, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059.

CHEN Bo, WANG Shuang, JIAO Licheng, et al. Polarimetric SAR image classification via weighted ensemble based on 0-1 matrix decomposition[J]. Journal of Electronics Information Technology, 2015, 37(6): 1495-1501. doi: 10.11999/JEIT141059.

崔浩贵, 刘涛, 蒋宇中, 等. 基于混合矩的极化SAR图像K分布模型参数估计新方法[J]. 电子与信息学报, 2015, 37(2): 328-333. doi: 10.11999/JEIT140551.

CUI Haogui, LIU Tao, JIANG Yuzhong, et al. Parameter estimation for the K-distribution in PolSAR imagery based on hybrid moments[J]. Journal of Electronics Information Technology, 2015, 37(2): 328-333. doi: 10.11999/JEIT140551.

杨学志, 叶铭, 吴克伟, 等. 结构保持的双边滤波极化SAR图像降噪[J]. 电子与信息学报, 2015, 37(2): 268-275. doi: 10.11999/JEIT140199.

YANG Xuezhi, YE Ming, WU Kewei, et al. Speckle reduction for PolSAR image based on structure preserving Bilateral filtering[J]. Journal of Electronics Information Technology, 2015, 37(2): 268-275. doi: 10.11999/JEIT140199.

刘璐, 刘帅, 焦李成, 等. 采用联合域字典稀疏表示的极化SAR图像分类[J]. 华中科技大学学报(自然科学版), 2016, 44(2): 81-85. doi: 10.13245/j.hust.160217.

LIU Lu, LIU Shuai, JIAO Licheng, et al. Combined dictionary learning based sparse representation for PolSAR image classification[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2016, 44(2): 81-85. doi: 10.13245/j.hust.160217.

RINGROSE R and HARRIS N. Ship detection using polarimetric SAR data[C]. Proceedings of SAR Workshop: CEOS Committee on Earth Observation Satellites; Working Group on Calibration and Validation, Toulouse, France, 2000, 450: 687-691.

HIROYUKI W, YUTA M, and KAZUKI N. Sea ice detection in the sea of Okhotsk using POLSAR and MODIS data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3): 1516-1523. doi: 10.1109/JSTARS.2013.2258327.

WANG Na, SHI Gongtao, LIU Li, et al. Plarimetric SAR target detection using the reflection symmetry[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(6): 1104-1108. doi: 10.1109/LGRS.2012.2189548.

文伟, 王英华, 冯博, 等. 基于监督非相干字典学习的极化SAR图像舰船目标检测[J]. 自动化学报, 2015, 41(11): 1926-1940. doi: 10.16383/j.aas.2015.c140530.

WEN Wei, WANG Yinghua, FENG Bo, et al. Supervise incoherent dictionary learning for ship detection with PolSAR images[J]. Acta Automatica Sinica, 2015, 41(11): 1926-1940. doi: 10.16383/j.aas.2015.c140530.

KHALID E D, PETER M, DESMOND P, et al. Target detection in synthetic aperture radar imagery: A state of the art survey[J]. Journal of Applied Remote Sensing, 2013, 7(1): 071598. doi: 10.1117/1.JRS.7.071598.

张学峰, 陈渤, 王鹏辉, 等. 一种基于Dirichlet过程隐变量支撑向量机模型的目标识别方法[J]. 电子与信息学报, 2015, 37(1): 29-36 doi: 10.11999/JEIT140129.

ZHANG Xuefeng, CHEN Bo, WANG Penghui, et al. A target recognition method based on Dirichlet process latent variable support vector machine model[J]. Journal of Electronics Information Technology, 2015, 37(1): 29-36 doi: 10.11999/ JEIT140129.

赵一博, 秦先祥, 邹焕新. 基于目标分解和SVM的极化SAR图像分类方法[J]. 航天返回与遥感, 2013, 34(2): 50-56.

ZHAO Yibo, QIN Xianxiang, and ZOU Huanxin. Classification of polarimetric SAR image based on target decomposition and SVM[J]. Journal of Spacecraft Recovery Remote Sensing, 2013, 34(2): 50-56.

XU Danlei, DU Lan, LIU Hongwei, et al. Bayesian classifier for sparsity-promoting feature selection[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(6): 1-28. doi: 10.1142/S0218001415500226.

LEE J S, MITCHELL R G, THOMAS L A, et al. Unsupervised classification using polarimetric decomposition and the complex wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249-2258. doi: 10.1109/36.789621.

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