基于深度特征的双极化SAR遥感图像岩性自动分类

李发森; 李显巨; 陈伟涛; 董玉森; 李雨柯; 王力哲

doi:10.3799/dqkx.2022.129

Volume 47 Issue 11

Nov. 2022

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Article Navigation > Earth Science > 2022 > 47(11): 4267-4279

Li Fasen, Li Xianju, Chen Weitao, Dong Yusen, Li Yuke, Wang Lizhe, 2022. Automatic Lithology Classification Based on Deep Features Using Dual Polarization SAR Images. Earth Science, 47(11): 4267-4279. doi: 10.3799/dqkx.2022.129

Citation:

Li Fasen, Li Xianju, Chen Weitao, Dong Yusen, Li Yuke, Wang Lizhe, 2022. Automatic Lithology Classification Based on Deep Features Using Dual Polarization SAR Images. Earth Science, 47(11): 4267-4279. doi: 10.3799/dqkx.2022.129

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Automatic Lithology Classification Based on Deep Features Using Dual Polarization SAR Images

doi: 10.3799/dqkx.2022.129

Li Fasen^{1
,
,},
Li Xianju¹,
Chen Weitao^{1
,
,},
Dong Yusen¹,
Li Yuke^{2
,
,},
Wang Lizhe¹

1.
School of Computer Science, China University of Geosciences, Wuhan 430078, China
2.
Mudanjiang Natural Resources Comprehensive Survey Center, China Geological Survey, Mudanjiang 157000, China

Received Date: 2022-05-15
Available Online: 2022-12-07
Publish Date: 2022-11-25

Abstract

Abstract

The lithology classification method based on pixel primitives, polarimetric synthetic aperture radar (SAR) data and traditional machine learning algorithm is easy to be affected by the inherent speckle noise, and the accuracy is not high. In order to reduce the effect of image noise, the neighborhood of large-scale pixels is considered as the primitive to characterize the spatial aggregation characteristics of surface geological units and the corresponding lithologic semantic information. Using GaoFen-3 dual polarization data, the polarization decomposition is carried out first, and a 3-channel color composite image is constructed as the input data of the subsequent model. Then, the deep convolutional neural network (DCNN) based migration learning method is used to extract the effective deep feature representation, so as to realize the automatic lithology classification under 5 m and 15 m spatial resolution conditions. The experiment results show that based on different resolution data and different DCNN algorithms, the total accuracy of automatic lithology classification is greater than 80%, and the highest accuracy is 91%. Generally, based on large-scale pixel neighborhood and DCNN migration learning method, high-precision lithology classification based on SAR data can be realized. The lithology remote sensing dataset based on dual polarization SAR created in this paper can also be used as the benchmark of lithology classification based on artificial intelligence.
- remote sensing,
- lithology classification,
- transfer learning,
- deep convolutional neural network,
- GaoFen-3,
- SAR

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References(59)

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