高概率选择和自适应MRF的极化SAR分类

doi:10.3969/j.issn.1001-2400.2017.06.011

西安电子科技大学学报

高概率选择和自适应MRF的极化SAR分类

张姝茵;侯彪

(西安电子科技大学智能感知与图像理解教育部重点实验室，陕西西安 710071)

收稿日期:2016-12-30 出版日期:2017-12-20 发布日期:2018-01-18
通讯作者: 侯彪(1974-), 男, 教授, 博士, E-mail: avcodec@163.com
作者简介:张姝茵(1982-), 女, 西安电子科技大学博士研究生, E-mail: zhangsy217@163.com．
基金资助:
国家自然科学基金资助项目(61671350)

POLSAR image classification via high-probability selection and adaptive MRF

ZHANG Shuyin;HOU Biao

(Ministry of Education Key Lab. of Intelligent Perception and Image Understanding, Xidian Univ., Xi'an 710071, China)

Received:2016-12-30 Online:2017-12-20 Published:2018-01-18

摘要/Abstract

摘要：

针对极化合成孔径雷达分类过程中较难同时获得精确的边缘和光滑的同质区域的问题，提出了一种基于Wishart距离的高概率选择分类器与自适应马尔科夫随机场相结合的分类方法，对极化合成孔径雷达图像分类．首先，将Wishart分类器应用于概率输出的支撑矢量机中，根据高概率选择得到一个基于像素的初始分类结果，并将此结果结合不同的边缘检测方法得到一个精确的边缘；其次，采用自适应窗口的马尔科夫随机场对上一步的分类结果进行修正，该过程在得到平滑区域的同时，也保持了上一步分类结果的边缘．实验结果表明，该算法提高了极化合成孔径雷达图像分类的精度，并保持了图像的细节信息．

关键词: 支撑矢量机, 极化合成孔径雷达, Wishart距离, 马尔科夫随机场, 自适应窗口

Abstract:

It is difficult to obtain the accurate boundaries and the smooth regions for polarimetric SAR image classification. In order to solve this problem, a novel classification scheme is proposed that combines the Wishart-based high-probabilistic support vector machine (SVM) and adaptive markov random fields(MRF). First, a Wishart classifier is applied with the probabilistic SVM, according to high-probalistic selection, an initial pixel-based classification result is obtained. Then by combining this result with other edge detection methods, it can access the accurate boundaries. Second, adaptive MRF is proposed based on the edge of the image to further revise the previous classification. In this way, smooth regions are obtained and accurate boundaries are maintained simultaneously. Experimental results show that the proposed method improves the classification performance and that details of the image are preserved.

Key words: SVM, polarimetric synthetic aperture radar, Wishart distance, MRF, adaptive window

张姝茵;侯彪. 高概率选择和自适应MRF的极化SAR分类[J]. 西安电子科技大学学报, 2017, 44(6): 59-64.

ZHANG Shuyin;HOU Biao. POLSAR image classification via high-probability selection and adaptive MRF[J]. Journal of Xidian University, 2017, 44(6): 59-64.

参考文献

［1］刘璐, 王蓉芳, 焦李成, 等. 采用联合相似度谱聚类集成的极化SAR分类［J］. 西安电子科技大学学报, 2015, 42(3): 48-53+128.
LIU Lu, WANG Rongfang, JIAO Licheng, et al. Combined Similarity Based Spectral Clustering Ensemble for POLSAR Classification［J］. Journal of Xidian University, 2015, 42(3): 48-53+128.
［2］ LEE J S, GRUNES M R, AINSWORTH T L, 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.
［3］陈博, 王爽, 焦李成, 等. 贝叶斯集成框架下的极化SAR图像分类［J］. 西安电子科技大学学报, 2015, 42(2): 45-51.
CHEN Bo, WANG Shuang, JIAO Licheng, et al. Polarimetric SAR Image Classification via Naive Bayes Combination［J］. Journal of Xidian University, 2015, 42(2): 45-51.
［4］宋婉莹, 李明, 张鹏, 等. 基于加权合成核与三重Markov场的极化SAR图像分类方法［J］. 电子学报, 2016, 44(3): 520-526.
SONG Wanying, LI Ming, ZHANG Peng, et al. A Classification Method of PolSAR Image Based on Weighted Composite Kernel and Triplet Markov Field［J］. Acta Electronica Sinica, 2016, 44(3): 520-526.
［5］ WU Y H, JI K F, YU W X, et al. Region-based Classification of Polarimetric SAR Images Using Wishart MRF［J］. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 668-672.
［6］ WU Z C, OUYANG Q D. SVM- and MRF-based Method for Contextual Classification of Polarimetric SAR Images［C］//Proceedings of the 2011 International Conference on Remote Sensing, Environment and Transportation Engineering. Piscataway: IEEE, 2011: 818-821.
［7］ LIU F, JIAO L C, HOU B, et al. POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information［J］. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3292-3308.
［8］ PLATT J C. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods［C］//Advances in Large Margin Classifiers. Cambridge: MIT Press, 1999: 61-74.
［9］ DING L, GOSHTASBY A. On the Canny Edge Detector［J］. Pattern Recognition, 2001, 34(3): 721-725.
［10］ GUO C E, ZHU S C, WU Y N. Primal Sketch: Integrating Texture and Structure［J］. Computer Vision and Image Understanding, 2007, 106(1): 5-19.
［11］ XU J, XIANG L, HANG R L, et al. Stacked Sparse Autoencoder(SSAE) Based Framework for Nuclei Patch Classification on Breast Cancer Histopathology［C］//Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging. Piscataway: IEEE, 2014: 999-1002.
［12］ LEE J S, GRUNES M R, KWOK R. Classification of Multi-look Polarimetric SAR Imagery Based on Complex Wishart Distribution［J］. International Journal of Remote Sensing, 1994, 15(11): 2299.

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[4]	邓燕子;卢朝阳;李静. 交通场景的多视觉特征图像分割方法[J]. J4, 2015, 42(6): 11-16.
[5]	刘璐;王蓉芳;焦李成;石俊飞. 采用联合相似度谱聚类集成的极化SAR分类[J]. J4, 2015, 42(3): 48-53+128.
[6]	陈博;王爽;焦李成;刘芳;毛莎莎. 贝叶斯集成框架下的极化SAR图像分类 [J]. J4, 2015, 42(2): 45-51.
[7]	崔浩贵;刘涛;单鸿昌;蒋宇中;高俊 . 一种PolSAR图像统计模型辨识新方法 [J]. J4, 2015, 42(2): 199-205.
[8]	毛莎莎;熊霖;焦李成;张爽;陈博. 利用旋转森林变换的异构多分类器集成算法[J]. J4, 2014, 41(5): 48-53.
[9]	郭睿;刘艳阳;臧博;邢孟道. 一种保持散射特性的极化SAR图像滤波方法[J]. J4, 2011, 38(1): 90-95.
[10]	李青;焦李成. 利用集成支撑矢量机提高分类性能 [J]. J4, 2007, 34(1): 68-70.
[11]	王永刚1;2;焦李成1. 基于支撑矢量机的TD-SCDMA系统多用户检测器[J]. J4, 2004, 31(5): 744-748.
[12]	丁爱玲;刘芳;郭兰英. 基于自适应投影算法和修正核函数算法的混合支撑矢量机[J]. J4, 2002, 29(4): 477-482.
[13]	汪西莉;刘芳;焦李成. 基于大规模数据的支撑矢量机的训练和分类[J]. J4, 2002, 29(1): 123-128.
[14]	丁爱玲;刘芳;姚霞. 基于支撑矢量机的智能目标识别方法[J]. J4, 2001, 28(6): 743-747.
[15]	暂时无作者信息. 支撑矢量机的分类机理研究[J]. J4, 2000, 27(7): 106-111.

高概率选择和自适应MRF的极化SAR分类

POLSAR image classification via high-probability selection and adaptive MRF

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