Recently, deep learning models, such as autoencoder, deep belief network and convolutional autoencoder (CAE), have been widely applied on polarimetric synthetic aperture radar (PolSAR) image classification task. These algorithms, however, only consider the amplitude information of the pixels in PolSAR images failing to obtain adequate discriminative features. In this work, a complex-valued convolutional autoencoder network (CV-CAE) is proposed. CV-CAE extends the encoding and decoding of CAE to complex domain so that the phase information can be adopted. Benefiting from the advantages of the CAE, CV-CAE extract features from a tiny number of training datasets. To further boost the performance, we propose a novel post processing method called spatial pixel-squares refinement (SPF) for preliminary classification map. Specifically, the majority voting and difference-value methods are utilized to determine whether the pixel-squares (PixS) needs to be refined or not. Based on the blocky structure of land cover of PolSAR images, SPF refines the PixS simultaneously. Therefore, it is more productive than current methods worked on pixel level. The proposed algorithm is measured on three typical PolSAR datasets, and better or comparable accuracy is obtained compared with other state-of-the-art methods.
Semi-Supervised Complex-Valued GAN for Polarimetric SAR Image Classification
