Polarimetric SAR Image Classification Based on Edge-Aware Dual Branch Fully Convolutional Network.

As a critical step for Polarimetric Synthetic Aperture Radar (PolSAR) images interpretation, PolSAR classification have attracted growing interests in the field of remote sensing. Recently, many novel ideas and models based on deep learning have emerged to solve the task of PolSAR image classification. The encode-decode network structure of Fully Convolutional Network (FCN) is proved to be effective for this task. However, due to the inherent disadvantages of FCN and the complex high-dimensional feature representation of PolSAR images, there are still some issues need to be addressed. Aiming at the problem that the edge of adjacent regions is not enough finely classified and the regional consistency of the same object class is relatively weak, we propose a novel PolSAR image classification method called DBFCN, which combines a well-designed edge-aware network and the improved FCN. The experimental results verify that it can effectively improve the classification accuracy of PolSAR images.