In this paper we are proposing classification algorithm for multifrequency Polarimetric Synthetic Aperture Radar (PolSAR) image. Using PolSAR decomposition algorithms 33 features are extracted from each frequency band of the given image. Then, a two-layer autoencoder is used to reduce the dimensionality of input feature vector while retaining useful features of the input. This reduced dimensional feature vector is then applied to generate superpixels using simple linear iterative clustering (SLIC) algorithm. Next, a robust feature representation is constructed using both pixel as well as superpixel information. Finally, softmax classifier is used to perform classification task. The advantage of using superpixels is that it preserves spatial information between neighbouring PolSAR pixels and therefore minimises the effect of speckle noise during classification. Experiments have been conducted on Flevoland dataset and the proposed method was found to be superior to other methods available in the literature.

翻译：本文提出了一种用于多频极化合成孔径雷达（PolSAR）图像的分类算法。利用PolSAR分解算法，从给定图像的每个频段中提取33个特征。随后，采用两层自编码器降低输入特征向量的维度，同时保留输入中的有用特征。将降维后的特征向量应用于简单线性迭代聚类（SLIC）算法以生成超像素。接着，利用像素和超像素信息构建鲁棒的特征表示。最后，使用softmax分类器执行分类任务。使用超像素的优势在于能够保留相邻PolSAR像素之间的空间信息，从而在分类过程中最小化散斑噪声的影响。在Flevoland数据集上进行了实验，结果表明所提方法优于文献中已有的其他方法。