Research on Multi-scale Residual UNet Fused with Depthwise Separable Convolution in PolSAR Terrain Classification

XIE Wen; WANG Ruonan; YANG Xin; LI Yongheng

doi:10.11999/JEIT220867

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 2975-2985

XIE Wen, WANG Ruonan, YANG Xin, LI Yongheng. Research on Multi-scale Residual UNet Fused with Depthwise Separable Convolution in PolSAR Terrain Classification[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2975-2985. doi: 10.11999/JEIT220867

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XIE Wen, WANG Ruonan, YANG Xin, LI Yongheng. Research on Multi-scale Residual UNet Fused with Depthwise Separable Convolution in PolSAR Terrain Classification[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2975-2985. doi: 10.11999/JEIT220867

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Research on Multi-scale Residual UNet Fused with Depthwise Separable Convolution in PolSAR Terrain Classification

doi: 10.11999/JEIT220867

School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61901365, 62071379), The Natural Science Foundation of Shaanxi Province (2019JQ-377), Shaanxi Provincial Department of Education Special Scientific Research Program (19JK0805), The New Star Team of Xi'an University of Posts and Telecommunications (xyt2016-01)

Received Date: 2022-06-29
Rev Recd Date: 2023-03-30

Available Online: 2023-04-04

Publish Date: 2023-08-21

Abstract

Abstract

As one of the important research contents of Synthetic Aperture Radar(SAR) image interpretation, Polarimetric Synthetic Aperture Radar(PolSAR) terrain classification has attracted more and more attention from scholars at home and abroad. Different from natural images, the PolSAR dataset not only has unique data attributes but also belongs to a small sample dataset. Therefore, how to make full use of the data characteristics and label samples is a key consideration. Based on the above problems, a new network on the basis of UNet for PolSAR terrain classification—Multiscale Separable Residual Unet(MSR-Unet) is proposed in this paper. In order to extract separately the spatial and channel features of the input data while reducing the redundancy of features, the ordinary 2D convolution is replaced by the depthwise separable convolution in MSR-Unet. Then, an improved multi-scale residual structure based on the residual structure is proposed. This structure obtains features of different scales by setting convolution kernels of different sizes, and at the same time the feature is reused by dense connection, using the structure can not only increase the depth of the network to a certain extent and obtain better features, but also enable the network to make full use of label samples and enhance the transmission efficiency of features information, thereby improving the classification accuracy of PolSAR terrain. The experimental results on three standard datasets show that compared with the traditional classification methods and other mainstream deep learning network models such as UNet, the MSR-Unet can improve average accuracy, overall accuracy and Kappa coefficient in different degrees and has better robustness.
- PolSAR terrain classification,
- UNet,
- Residual structure,
- Depthwise separable convolution

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References(28)

References

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