Auto encoder feature learning with utilization of local spatial information and data distribution for classification of PolSAR image

Abstract

The distribution of data plays a key role in the designing of a machine learning model. Therefore, this paper proposes a novel auto encoder network based on the distribution of polarimetric synthetic aperture radar (PolSAR) data matrix. Designed specifically for PolSAR data matrix, the proposed mixture auto encoder (MAE) feature learning method defines data error term in the loss function according to the data distribution. Instead of the pixel itself, all pixels in the neighborhood are used as input to train the proposed MAE. Then, a corresponding classification network is also given by discarding the decoder process of the proposed MAE and connecting with a Softmax classifier. The MAE is trained using the unlabeled data, while the training process of the classification network is completed with the help of a small number of labeled pixels. In view of the phenomenon of misclassification in the predicted result image, two post-processing steps acting on local spatial are also given, which accomplished by the proposed two filters. Extensive experiments by four methods were made over three real PolSAR images including the proposed classification network. The experimental results show that introducing data distribution into the auto encoder network leads to an average 4% improvement in overall accuracy for three PolSAR images. Moreover, the post-processing steps with the proposed filters bring a new level of discrimination on the classification performance of PolSAR images.