极化SAR图像精细地物分类方法研究与实现
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摘要
极化合成孔径雷达(PolSAR)是近年来遥感领域最为先进的传感器之一。极化SAR图像分类是极化SAR图像解译的重要研究内容和关键技术之一,在民用和军事领域均有重大的理论意义和应用价值。本文针对近年来国内外极化SAR图像分类方法中存在的一些问题,从空间相关性、空间自适应性和类别数目自适应性三个方面对极化SAR地物分类技术展开研究,主要的工作和贡献如下:
1)充分考虑了图像中像素与像素之间的空间相关性,引入了计算机视觉领域的超像素概念。根据极化SAR数据所特有的统计特性,有效提取图像中的边缘信息,并结合归一化切割准则,提出一种基于超像素的极化SAR图像监督分类方法。该方法分类结果清晰,易于理解;
2)结合H/a-Wishart聚类、四叉树分解和Wishart马尔科夫随机场(Wishart-MRF),提出了一种能够自适应空间复杂度的极化SAR图像分割方法,该方法具有一定的空间自适应性,能够有效保留图像中的细节信息;
3)介绍了知识与数据挖掘领域中一种可视化的聚类趋势分析方法,并结合超像素的生成,提出一种基于超像素的极化SAR图像地物类别数目估计与分类方法。该方法在无先验知识的指导下,不但能够较为准确估计出极化SAR图像中地物的类别数目,而且可以快速确定每一种地物类别的聚类中心并以此为基础进行无监督分类。分类的准确度较高,分类结果易于理解和进一步分析。此外,还将该方法拓展到高分辨率SAR领域,结合灰度和纹理特征分析,进行高分SAR图像地物类别数目估计和分类,也取得了较好的结果。
Polarimetric synthetic aperture radar(PolSAR)is one of the most advanced remote sensors in recent years.As an essential part and a key technology for PolSAR image interpretation, PolSAR image classification has been playing an important role in many fields of both civil and military applications. According to some problems of PolSAR image classification methods in recent years, we primarily study on the classification technology from three aspects:spatial relationship, spatial complexity adaptive and number-of-classes adaptive. The main work and contributions accomplished in this paper are as follows:
1) We take the spatial relations between pixels into consideration and introduce the concept of superpixel in the field of computer vision. With good use of the inherent statistical characteristics and contour information of PolSAR data, we present a novel superpixel-based PolSAR image classification method using Normalized Cut. The classification results are very clear and easy to understand.
2) Incorporating H/α-Wishart clustering, quad-tree decomposition and Wishart markov random field theory, we present a complexity adaptive segmentation method for PolSAR Images. The method integrates spatial adaptivity and the experimental results show that it can keep information of the details in PolSAR images.
3) We first introduce a tool in the field of knowledge and data engineering, which is Visual Assessment of Cluster Tendency. Then incorporating superpixel generation method, we present a novel superpixel -based classification framework with an adaptive number of classes for PolSAR images. Although without any guidance of prior knowledge, this method can effectively estimate the number of classes and each class center in the image. Then we can use these for unsupervised classification of PolSAR images. This framework is capable of improving the classification accuracy, making the results more understandable and easier for further analysis. Additionally, we apply this framework for high-resolution SAR images. Combined with analysis of gray-scale and texture features, we also make it work well for high-resolution SAR images. The experiment result shows that the proposed method provides a promising performance for high-resolution SAR image classification.
1)充分考虑了图像中像素与像素之间的空间相关性,引入了计算机视觉领域的超像素概念。根据极化SAR数据所特有的统计特性,有效提取图像中的边缘信息,并结合归一化切割准则,提出一种基于超像素的极化SAR图像监督分类方法。该方法分类结果清晰,易于理解;
2)结合H/a-Wishart聚类、四叉树分解和Wishart马尔科夫随机场(Wishart-MRF),提出了一种能够自适应空间复杂度的极化SAR图像分割方法,该方法具有一定的空间自适应性,能够有效保留图像中的细节信息;
3)介绍了知识与数据挖掘领域中一种可视化的聚类趋势分析方法,并结合超像素的生成,提出一种基于超像素的极化SAR图像地物类别数目估计与分类方法。该方法在无先验知识的指导下,不但能够较为准确估计出极化SAR图像中地物的类别数目,而且可以快速确定每一种地物类别的聚类中心并以此为基础进行无监督分类。分类的准确度较高,分类结果易于理解和进一步分析。此外,还将该方法拓展到高分辨率SAR领域,结合灰度和纹理特征分析,进行高分SAR图像地物类别数目估计和分类,也取得了较好的结果。
Polarimetric synthetic aperture radar(PolSAR)is one of the most advanced remote sensors in recent years.As an essential part and a key technology for PolSAR image interpretation, PolSAR image classification has been playing an important role in many fields of both civil and military applications. According to some problems of PolSAR image classification methods in recent years, we primarily study on the classification technology from three aspects:spatial relationship, spatial complexity adaptive and number-of-classes adaptive. The main work and contributions accomplished in this paper are as follows:
1) We take the spatial relations between pixels into consideration and introduce the concept of superpixel in the field of computer vision. With good use of the inherent statistical characteristics and contour information of PolSAR data, we present a novel superpixel-based PolSAR image classification method using Normalized Cut. The classification results are very clear and easy to understand.
2) Incorporating H/α-Wishart clustering, quad-tree decomposition and Wishart markov random field theory, we present a complexity adaptive segmentation method for PolSAR Images. The method integrates spatial adaptivity and the experimental results show that it can keep information of the details in PolSAR images.
3) We first introduce a tool in the field of knowledge and data engineering, which is Visual Assessment of Cluster Tendency. Then incorporating superpixel generation method, we present a novel superpixel -based classification framework with an adaptive number of classes for PolSAR images. Although without any guidance of prior knowledge, this method can effectively estimate the number of classes and each class center in the image. Then we can use these for unsupervised classification of PolSAR images. This framework is capable of improving the classification accuracy, making the results more understandable and easier for further analysis. Additionally, we apply this framework for high-resolution SAR images. Combined with analysis of gray-scale and texture features, we also make it work well for high-resolution SAR images. The experiment result shows that the proposed method provides a promising performance for high-resolution SAR image classification.
引文
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[2]张直中.微波成像术.北京:科学出版社,1990.
[3] Zebker H A and van Zvl J J.Imaging radar polarimetry:a review.Proc.IEEE,199l,79(11):1583-1606.
[4] Touzi R.Boerner WM.Lee J S,el a1.A review of polarimetry in the context of synthetic aperture radar:concepts and information extraction.Canadian Journal of Remote Sensing,2004,30(3):380-407.
[5] van Zvl J J and Zebker H A.Imaging radar polarization signatures:theory and observation.Radio Science.1987.22:529-543.
[6] Lee JS. A review of Polarimetric SAR algorithms and their applications.Taiwan Journal of Photogrammetry and Remote Sensing,2004,9(3):31-80.
[7] J. J. van Zyl. Unsupervised classification of scattering behavior using radar polarimetry data, IEEE Trans. Geosci. Remote Sens., vol. 27, no. 1, pp. 36–45, Jan. 1989.
[8] S. R. Cloude and E. Pottier. An entropy based classification scheme for land applications of polarimetric SAR, IEEE Trans. Geosci. Remote Sens., vol. 35, no. 1, pp. 68–78, Jan. 1997.
[9] L. Ferro-Famil, E. Pottier, and J. S. Lee. Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha Wishart classifier, IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, pp. 2332–2342, Nov. 2001.
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[13] J. S. Lee, M. R. Grunes, T. L. Ainsworth, L.-J. Du, D. L. Schuler, and S. R. Cloude. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier, IEEE Trans. Geosci. Remote Sens., vol. 37, no. 5, pp. 2249–2258, Sep. 1999.
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[15] G. Vasile, J.-P. Ovarlez, F. Pascal, and C. Tison. Coherency matrix estimation of heterogeneous clutter in high-resolution polarimetric SAR images, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1809–1826, Apr. 2010.
[16] P. Formont, F. Pascal, G. Vasile, J.-P. Ovarlez, and L. Ferro-Famil. Statistical classification for heterogeneous polarimetric SAR images, IEEE Journal of Selected Topics in Signal Processing, to be appeared.
[17] L. Bombrun, G. Vasile, M. Gay, and F. Totir. Hierarchical segmentation of polarimetric SAR images using heterogeneous clutter models, IEEE Trans. Geosci. Remote Sens. vol. 49, no. 2, pp. 726–737, Feb. 2011.
[18] U. Benz and E. Pottier. Object based analysis of polarimetric SAR data in alpha-entropy-anisotropy decomposition using fuzzy classification by eCognition, 2001 IGARSS’01 IEEE International, 10.1109/IGARSS.2001.976867
[19] D. G. Corr, A. Walker, U. Benz, I. Lingenfelder, and A. Rodrigues. Classification of urban SAR imagery using object oriented techniques, 2003 IGRASS IEEE International, 10.1109/IGARSS.2003.1293719
[20] L. Ferro-Famil and E. Pottier. Urban area remote sensing from L-band PolSAR data using Time-Frequency techniques, Urban Remote Sensing Joint Event, 10.1109/URS.2007.371769
[21] R. Sato and K. Soma. Classification of stricken residential houses by the mid niigata prefecture earthquake based on POLSAR image analysis, 2007 IGARSS IEEE International, 10.1109/IGARSS.2007.4422764
[22] G. Singh and G. Venkataraman. LOS PALSAR data analysis of snow cover area in Himalayan region using four component scattering decomposition technique, Recent Advances in Microwave Theory and Applications, 10.1109/AMTA.2008.4763078
[23] R. Sato and Y. Yamaguchi. Seasonal change monitoring of wetlands by using airborne and satellite PolSAR sensing IGARSS 2008 IEEE International 10.1109/IGARSS.2008.4778984
[24] M. Jager and M. Neumann. The distribution of interferometric phase differentialsand a self-initializing PolInSAR classifier, Radar Conference, 2005 European, 10.1109/ EURAD.2005.1605595
[25] M. Neumann, A. Reigber, and L. Ferro-Famil. Data classification based on PolInSAR coherence shapes, IGARSS 2005 IEEE International, 10.1109/IGARSS.2005.1526760
[26] J.C. Souyris, P. Imbo, R. Fjortoft, M. Sandra, and J. S. Lee. Compact polarimetry based on symmetry properties of geophysical media:π/4 mode, IEEE Trans. Geosci. Remote Sens. 2005, 10.1109/TGRS.2004.842486
[27]陈思,杨健,宋小全. SAR图像线特征分析与自动提取,系统工程与电子技术,第32卷,第9期,2010年9月
[28]安文韬,才长帅,杨健.极化SAR图像的人工目标检测,清华大学学报,2010年第50期第4卷
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