针对极化SAR图像训练样本数目较少问题以及极化SAR图像同质区域较多的特性,提出了一种新的两层分类框架,结合了稀疏自编码器和边缘保持的Wishart马尔科夫随机场对极化SAR图像进行分类.该框架包括个步骤,第一个步骤使用稀疏自编码器来获得一个初始分类；第二个步骤使用边缘保持的Wishart马尔科夫随机场对第一层的分类结果进行修正.在应用Wishart马尔科夫随机场的过程中,由稀疏自编码器分类得到的边缘得以保持,并且提出了新的分类错误纠正策略确保分类的准确性.因此,通过稀疏自编码器得到的精确分类边缘可用于不同的区域并且在应用Wishart马尔科夫的过程中得以保持.和其他分类方法相比,该方法得到较高的分类精度,证明了新方法的有效性.

In order to solve problem of the limited training samples and keep consistency in one region, a new two-level classification scheme is proposed, which combines sparse auto-encoder (SAE) and Boundary-preserved Wishart-markov random fields (BWMRF). In the first layer, an SAE classifier is applied to obtain an initial classification and more accurate regional boundaries. In the second layer, Boundary-preserved Wishart-markov random fields have been used to correct the previous classification results. Meanwhile, the boundaries classified by sparse auto-encoder are preserved, and a new error correction strategy is applied to ensure the classification accuracy. Therefore, accurate region boundaries supplied by SAE are explored to divide different regions, and the coherent in each region will be realized during the BWMRF process. Compared with other classification methods, this method obtains higher classification accuracy and proves the validity of the new scheme.