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.

我们提出了一个多频率极化合成孔径雷达图像分类算法，使用PolSAR分解算法从给定图像的每个频带提取了33个特征。接着，我们使用两层自编码器来减少输入特征向量的维度，同时保留有用的特征。然后，我们应用简单的线性迭代聚类算法（SLIC）生成超像素。接下来，我们使用像素和超像素信息构建稳健的特征表示。最后，我们使用softmax分类器执行分类任务。使用超像素的优点在于它能保留相邻PolSAR像素之间的空间信息，从而在分类过程中最小化斑点噪声的影响。我们在Flevoland数据集上进行了实验，结果表明我们提出的方法优于文献中其他方法。

基于堆叠自编码器的多频极化合成孔径雷达图像特征提取与超像素生成