Classification of PolSAR Image Using Neural Nonlocal Stacked Sparse Autoencoders with Virtual Adversarial Regularization

Polarimetric synthetic aperture radar (PolSAR) has become increasingly popular in the past two decades, for it can derive multichannel features of ground objects, which contains more discriminative information compared with traditional SAR. In this paper, a neural nonlocal stacked sparse autoencoders with virtual adversarial regularization (NNSSAE-VAT) is proposed for PolSAR image classification. The NNSSAE first extracts the nonlocal features by calculating pairwise similarity of each pixel and its surrounding pixels using a neural network, which contains a multiscale feature extractor and a linear embedding layer. The feature extraction process can relieve the negative influence of speckle noise and extract discriminative nonlocal spatial information without carefully designed parameters. Then, the SSAE maps the center pixel and the extracted nonlocal features into deep latent space in which a Softmax classifier is utilized to conduct classification. The virtual adversarial training is introduced to regularize the network, which tries to keep the network from being overfitting. The experimental results from three real PolSAR image show that the proposed NNSSAE-VAT method has proved its robustness and effectiveness and it can achieve competitive performance compared with related methods.