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
Bibliographical noteThis work was supported in part by the Beijing Nova Program under Grant Z201100006820016; in part by the National Key Research and Development of China under Grant 2018AAA0102100; in part by the National Natural Science Foundation of China under Grant 62002014, Grant 61532005, Grant U1936212, Grant 61971016, Grant U1803264, Grant 61922046, Grant 61772344, and Grant 61672443; in part by the Hong Kong Research Grants Council (RGC) General Research Funds under Grant 9042816 and Grant CityU 11209819; in part by the Fundamental Research Funds for the Central Universities under Grant 2019RC039 and Grant 2018JBZ001; in part by the Elite Scientist Sponsorship Program by the Beijing Association for Science and Technology; in part by the Hong Kong Scholars Program, Zhejiang Laboratory under Grant 2019NB0AB01; in part by the Tianjin Natural Science Foundation under Grant 18ZXZNGX00110; and in part by the China Postdoctoral Science Foundation under Grant 2020T130050 and Grant 2019M660438.
- dense attention fluid
- global context-aware attention
- optical remote sensing images
- Salient object detection