High-Resolution SAR Image Classification Using Context-Aware Encoder Network and Hybrid Conditional Random Field Model
Wenkai Liang, Yan Wu, Ming Li, Yice Cao
Abstract
A pixel-wise classification for high-resolution (HR) synthetic aperture radar (SAR) images is a challenging task, due to the limited availability of labeled SAR data, as well as the difficulty of exploring context information affected by coherent speckle. In this article, we propose a novel supervised classification method for HR SAR images, which combines a context-aware encoder network (CAEN) and a hybrid conditional random field (HCRF) model. First, a new CAEN architecture is developed based on the intrinsic property of HR SAR pixel-wise labeling. The proposed architecture follows an encoder-decoder structure, wherein the residual context encoder (RCE) block and the global context-aware (GCA) block are proposed in the encoder module to capture local to global semantic contexts. The multiscale skip connections and feature compression structures are designed in the decoder module to preserve precise object structures while improving computational efficiency. Then, a patch sampling strategy is adopted to ensure that the training and test data are completely separated. It can achieve a less-biased estimate of the test error of CAEN. In addition, the overlapped sampling and data augmentation techniques are used to solve the problem of limited labeled data. Finally, the HCRF model is constructed and combined with the previous CAEN to further enhance the spatial label consistency. Our HCRF integrates pixel-level and region-level potentials into a unified Bayesian framework, making two spatial supports come to a more accurate decision on pixel categories. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms.