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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

Clc Number:

TP79

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    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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History
  • Received:January 02,2022
  • Revised:January 13,2022
  • Adopted:January 15,2022
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