Attention-Aware Three-Branch Network for Salient Object Detection in Remote Sensing Images

Abstract

Although remarkable advances have been achieved on salient object detection (SOD) for natural scene images (NSIs), SOD for optical remote sensing images (RSIs) still remains a big challenge due to the unique imaging conditions and various scene patterns. To enable effective SOD for RSIs, this letter proposes a novel end-to-end network, called attention-aware three-branch network (AATBNet). First, an attention feature encoding branch is constructed for learning more discriminative features. Then, a hierarchical feature decoding branch, equipped with three streams, i.e., a decoding stream, a dilated reverse attention stream, and a fusion dense up-sampling convolution stream, is proposed to effectively and robustly compute saliency maps and salient edge maps. Third, a two losses computation branch is designed to further boost SOD performance. Comprehensive evaluations on two well-known RSIs benchmarks, as well as comparisons with 20 state-of-the-art technologies validate the superiority of our AATBNet. The code of our method is publicly available at: https://github.com/WangXin81/AATBNet.