SAR Image Change Detection Based On URNet Network
Pages 49 - 55
Abstract
In order to improve the accuracy of synthetic aperture radar (SAR) image change detection based on deep convolution neural network. In this paper, a SAR image change detection method based on U-Rank Network (URNet) is proposed. Firstly, the convolution unit of U-shaped network is used to extract the features of the input image, and then the size of the input features is scaled by down-sampling(DS) part and up-sampling(US) part. Secondly, inspired by squeeze and excitation(SE) network, rank operation is added on the basis of SE module, and the relationship between input feature information channels is further used to enhance channel information. Then, the output features of each branch are restored to the same size by resampling unit. Finally, the features of each branch are fused together, and the final result graph is obtained through the whole connection layer. The experimental results of three groups of different types of SAR show that the method proposed in this paper is superior to other compared methods.
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Index Terms
- SAR Image Change Detection Based On URNet Network
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Published In
December 2022
189 pages
ISBN:9781450397568
DOI:10.1145/3579109
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Published: 14 March 2023
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ICVIP 2022
ICVIP 2022: 2022 The 6th International Conference on Video and Image Processing
December 23 - 26, 2022
Shanghai, China
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