A Robust Polarmetric SAR Terrain Classification Based on Sparse Deep Autoencoder Model Combined With Wavelet Kernel-Based Classifier

Since the existing terrain classification algorithm based on deep learning is not ideal for unbalanced PolSAR classification, a effective terrain classification algorithm based on wavelet kernel sparse deep coding network under unbalanced data set is proposed in this paper. The algorithm firstly adopts a structured sparse operation so as to enhance the accuracy of feature propagation and reduce the amount of stored data, where the unimportant parameter connections in each group are gradually reduced by dividing the network convolution kernel into multiple groups during the training process; The wavelet kernel-based classifier is used instead of the Sigmoid function to classify and identify features for different terrain, which has high generalization performance for small sample, nonlinear and high-dimensional mode classification problems. The experimental results show that our proposed classification algorithm can improve the classification performance of unbalanced samples, and improve the classification efficiency while ensuring the accuracy of classification.