  Despite the remarkable advances in visual saliency analysis for natural scene
images (NSIs), salient object detection (SOD) for optical remote sensing images
(RSIs) still remains an open and challenging problem. In this paper, we propose
an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A
Global Context-aware Attention (GCA) module is proposed to adaptively capture
long-range semantic context relationships, and is further embedded in a Dense
Attention Fluid (DAF) structure that enables shallow attention cues flow into
deep layers to guide the generation of high-level feature attention maps.
Specifically, the GCA module is composed of two key components, where the
global feature aggregation module achieves mutual reinforcement of salient
feature embeddings from any two spatial locations, and the cascaded pyramid
attention module tackles the scale variation issue by building up a cascaded
pyramid framework to progressively refine the attention map in a coarse-to-fine
manner. In addition, we construct a new and challenging optical RSI dataset for
SOD that contains 2,000 images with pixel-wise saliency annotations, which is
currently the largest publicly available benchmark. Extensive experiments
demonstrate that our proposed DAFNet significantly outperforms the existing
state-of-the-art SOD competitors. https://github.com/rmcong/DAFNet_TIP20
