Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index
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Published:2023-06-21
Issue:13
Volume:15
Page:3221
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Huang Yabo123, Meng Mengmeng123, Hou Zhuoyan1, Wu Lin123ORCID, Guo Zhengwei123, Shen Xiajiong123, Zheng Wenkui4, Li Ning123ORCID
Affiliation:
1. School of Computer and Information Engineering, Henan University, Kaifeng 475004, China 2. Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China 3. Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China 4. School of Software, Henan University, Kaifeng 475004, China
Abstract
Accurate land cover classification (LCC) is essential for studying global change. Synthetic aperture radar (SAR) has been used for LCC due to its advantage of weather independence. In particular, the dual-polarization (dual-pol) SAR data have a wider coverage and are easier to obtain, which provides an unprecedented opportunity for LCC. However, the dual-pol SAR data have a weak discrimination ability due to limited polarization information. Moreover, the complex imaging mechanism leads to the speckle noise of SAR images, which also decreases the accuracy of SAR LCC. To address the above issues, an improved dual-pol radar vegetation index based on multiple components (DpRVIm) and a new LCC method are proposed for dual-pol SAR data. Firstly, in the DpRVIm, the scattering information of polarization and terrain factors were considered to improve the separability of ground objects for dual-pol data. Then, the Jeffries-Matusita (J-M) distance and one-dimensional convolutional neural network (1DCNN) algorithm were used to analyze the effect of difference dual-pol radar vegetation indexes on LCC. Finally, in order to reduce the influence of the speckle noise, a two-stage LCC method, the 1DCNN-MRF, based on the 1DCNN and Markov random field (MRF) was designed considering the spatial information of ground objects. In this study, the HH-HV model data of the Gaofen-3 satellite in the Dongting Lake area were used, and the results showed that: (1) Through the combination of the backscatter coefficient and dual-pol radar vegetation indexes based on the polarization decomposition technique, the accuracy of LCC can be improved compared with the single backscatter coefficient. (2) The DpRVIm was more conducive to improving the accuracy of LCC than the classic dual-pol radar vegetation index (DpRVI) and radar vegetation index (RVI), especially for farmland and forest. (3) Compared with the classic machine learning methods K-nearest neighbor (KNN), random forest (RF), and the 1DCNN, the designed 1DCNN-MRF achieved the highest accuracy, with an overall accuracy (OA) score of 81.76% and a Kappa coefficient (Kappa) score of 0.74. This study indicated the application potential of the polarization decomposition technique and DEM in enhancing the separability of different land cover types in SAR LCC. Furthermore, it demonstrated that the combination of deep learning networks and MRF is suitable to suppress the influence of speckle noise.
Funder
the National Natural Science Foundation of China the Plan of Science and Technology of Henan Province the Key Laboratory of Natural Resources Monitoring and Regulation in Southern Hilly Region, the Ministry of Natural Resources of the People’s Republic of China National Undergraduate Training Program for Innovation and Entrepreneurship the Key Laboratory of Land Satellite Remote Sensing Application, the Ministry of Natural Resources of the People’s Republic of China
Subject
General Earth and Planetary Sciences
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