Subspace Learning Network: An Efficient ConvNet for PolSAR Image Classification

Land cover classification is an important part of the polarimetric synthetic aperture radar (PolSAR) image interpretation. The convolutional neural network (CNN) has been utilized to improve the classification accuracy recently. However, how to efficiently train the classification model with limited training samples while keeping the generalization performance is still a challenge. In this letter, we devise a subspace learning network (SSLNet) for PolSAR image classification, which can be trained more efficiently. First, a third-order polarimetric feature tensor is constructed using five-target decompositions to make full use of the prior knowledge. The tensor is then fed into a two-layer CNN in which the principal component analysis (PCA) is employed to learn the convolutional filters. Finally, the output features of the network are obtained by binary hashing and block-wise histograms, followed by the nearest neighbor (NN) classifier to complete the classification. Due to the simple learning strategy, the proposed SSLNet can be easily designed and efficiently trained. Experimental results on benchmark PolSAR data reveal that the SSLNet can achieve higher classification accuracy with limited training samples than the conventional CNN method.