Classification of Polarimetric SAR Images Based on Modeling Contextual Information and Using Texture Features
Classification of Polarimetric SAR Images Based on Modeling Contextual Information and Using Texture Features
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DOI：
10.1109/tgrs.2015.2469691
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发表时间：
2016-02
影响因子：
8.2
通讯作者：
A. Masjedi;M. J. V. Zoej;Y. Maghsoudi
中科院分区：
文献类型：
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作者：
A. Masjedi;M. J. V. Zoej;Y. Maghsoudi
提出了一种基于上下文的极化合成孔径雷达数据分类方法。该方法结合了支持向量机（SVM）和Wishart分类器，从参数方法和非参数方法中获益。该方法利用Wishart分布计算像素邻域马尔可夫随机场（MRF）的能量函数。然后将马尔可夫能量差函数与支持向量机分类器联系起来。因此，使用上下文分类器减少了对分类地图的盐和胡椒效应。此外，为了充分利用空间信息，在上下文分类中加入了纹理特征。从SPAN图像中提取纹理特征并将其添加到SVM分类器中。本文以某森林地区的两幅Radarsat-2偏振图像为研究对象，分别在落叶季和落叶季获取。在支持向量机中利用复合核挖掘有效的多时信息。与Wishart、Wishart- mrf、SVM和SVM复合核分类器相比，整体准确率分别提高了21.72%、16.17%、11.29%和8.19%。此外，与仅使用极化特征相比，将纹理特征纳入分类结果显著提高了森林物种的平均精度。
This paper proposes a novel contextual method for classification of polarimetric synthetic aperture radar data. The method combines support vector machine (SVM) and Wishart classifiers to benefit from both parametric and nonparametric methods. This method computes the energy function of a Markov random field (MRF) in the neighborhoods of the pixel using Wishart distribution. It then relates the Markovian energy difference function to the SVM classifier. Therefore, the salt-and-pepper effect on the classified map is reduced using a contextual classifier. Moreover, to achieve the full advantage of spatial information, texture features are added into the contextual classification. Texture features are extracted from SPAN images and are added to the SVM classifier. In this paper, two Radarsat-2 polarimetric images acquired in the leaf-off and leaf-on seasons are used from a forest area. Efficient multitemporal information is exploited using composite kernels in SVM. Comparison of the proposed algorithm with the Wishart, Wishart-MRF, SVM, and SVM with composite kernel classifiers shows a 21.72%, 16.17%, 11.29%, and 8.19% improvement in overall accuracy, respectively. Moreover, incorporating texture features into classification results significant increase in the average accuracy in forest species compared with the use of only polarimetric features.