Progressive Context-Aware Dynamic Network for Salient Object Detection in Optical Remote Sensing Images

Although remarkable progress has been made for salient object detection (SOD) in optical remote sensing images (RSIs), the static network design paradigm adopted by existing methods would limit their adaptability to large variations in remote sensing scenes as well as object appearances. In contrast, we explore this research issue from the perspective of generating dynamic network filters in which the parameters are conditioned on specific scene- and location-level contexts. In this paper, we propose a Progressive Context-aware Dynamic Network (PCD-Net) for SOD in RSIs, which adaptively captures context information and adjusts its filtering parameters for saliency detection. PCD-Net adopts an encoder-decoder architecture in which encoded feature representations are progressively decoded by a newly proposed dynamic module, namely Pyramid Scene- and Location-sensitive Dynamic filtering module (PSLD), to generate saliency representations. Furthermore, to transfer effective features from the encoder to the decoder, we construct a Dynamic Transfer Attention (DTA) module to control the interference between the encoder and the decoder in a more flexible way. Extensive evaluations on two commonly-used benchmarks demonstrate the superiority of the proposed method against the existing state-of-the-art methods.