Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

• URL: http://arxiv.org/abs/2011.13144v1
• Date: Thu, 26 Nov 2020 06:14:10 GMT
• Title: Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
• Authors: Qijian Zhang, Runmin Cong, Chongyi Li, Ming-Ming Cheng, Yuming Fang, Xiaochun Cao, Yao Zhao, and Sam Kwong
• Abstract summary: We propose an end-to-end Dense Attention Fluid Network (DAFNet) for salient object detection in optical remote sensing images (RSIs) A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships. We construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations.
• Score: 193.77450545067967
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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