基于RFA-LinkNet模型的高分遥感影像水体提取
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1.南京信息工程大学遥感与测绘工程学院;2.淮阴工学院计算机与软件工程学院;3.上海航天电子技术研究所

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TP79

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国家自然科学基金(41971414;S62076107)江苏省研究生科研与实践创新计划(KYCX20_0976);


RFA-LinkNet: A Novel Deep Learning Network for Water-body Extraction from High-Resolution Remote Sensing Images
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1.School of Remote Sensing Geomatics Engineering,Nanjing University of Information Science Technology,Nanjing ,China;2.School of Remote Sensing Geomatics Engineering,Nanjing University of Information Science Technology,Nanjing ,China;3.School of Computer and Software Engineering,Huaiyin Institute of Technology;4.Shanghai Aerospace Electronics Research Institute

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    摘要:

    针对现阶段卷积神经网络模型,在复杂地物背景下水体提取精度低,多尺度特征捕获能力差,模型复杂的问题,基于LinkNet模型提出一种结合RFB(Receptive Field Block)模块和通道注意力机制(Channel Attention Block)的FRA-LinkNet(Receptive Field Attention LinkNet)高分辨率光学遥感影像水体提取模型。首先,将RFB模块用于获取高阶水体语义信息与多尺度特征;其次,利用通道注意力机制,对特征编码和解码的特征进行加权融合,抑制背景特征,增强水体语义。与现有卷积神经网络模型相比,提出方法不仅具有高效的性能和鲁棒性,而且能实现高精度的水体提取。

    Abstract:

    In order to solve the problems of low water-body extraction accuracy, poor multi-scale feature capture ability, high model complexity of Convolutional Neural Network (CNN) in complex background image, a Receptive Field Attention LinkNet (FRA-LinkNet) is proposed. FRA-LinkNet is a high-resolution optical remote sensing image water-body extraction model, which is a combination of Receptive Field Block (RFB) and Channel Attention Block (CAB) based on LinkNet. Firstly, RFB is used to obtain high-level water-body semantic information and multi-scale feature map; Secondly, CAB is added to weighted aggregate encoding and decoding features, to suppress background features and enhance water-body semantics. Compared with state-of-the-art CNN model, the proposed RFA-LinkNet has high performance, robustness, and precision water-body extraction.

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历史
  • 收稿日期:2022-01-02
  • 最后修改日期:2022-01-13
  • 录用日期:2022-01-15
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