Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization
Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cyc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2304 |
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| author | Qiang Yin Yuming Du Fangfang Li Yongsheng Zhou Fan Zhang |
| author_facet | Qiang Yin Yuming Du Fangfang Li Yongsheng Zhou Fan Zhang |
| author_sort | Qiang Yin |
| collection | DOAJ |
| description | Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, the actual planting of crops often shows spatial dispersion, and the same crop may be dispersed in different plots, which fails to adequately consider the correlation information between dispersed plots of the same crop in spatial distribution. This study proposed a crop classification method based on multi-temporal dual polarimetric data, which considered the utilization of information between near and far spatial plots, by employing superpixel segmentation and a HyperGraph neural network, respectively. Firstly, the method utilized the dual polarimetric covariance matrix of multi-temporal data to perform superpixel segmentation on neighboring pixels, so that the segmented superpixel blocks were highly compatible with the actual plot shapes from a long-term period perspective. Then, a HyperGraph adjacency matrix was constructed, and a HyperGraph neural network (HGNN) was utilized to better learn the features of plots of the same crop that are distributed far from each other. The method fully utilizes the three dimensions of time, polarization and space information, which complement each other so as to effectively realize high-precision crop classification. The Sentinel-1 experimental results show that, under the optimal parameter settings, the classified accuracy of combined temporal superpixel scattering features using the HGNN was obviously improved, considering the near and far distance spatial correlations of crop types. |
| format | Article |
| id | doaj-art-130a9888f5764cf19ac4b0352829b70e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-130a9888f5764cf19ac4b0352829b70e2025-08-20T02:36:27ZengMDPI AGRemote Sensing2072-42922025-07-011713230410.3390/rs17132304Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive UtilizationQiang Yin0Yuming Du1Fangfang Li2Yongsheng Zhou3Fan Zhang4College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaDual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, the actual planting of crops often shows spatial dispersion, and the same crop may be dispersed in different plots, which fails to adequately consider the correlation information between dispersed plots of the same crop in spatial distribution. This study proposed a crop classification method based on multi-temporal dual polarimetric data, which considered the utilization of information between near and far spatial plots, by employing superpixel segmentation and a HyperGraph neural network, respectively. Firstly, the method utilized the dual polarimetric covariance matrix of multi-temporal data to perform superpixel segmentation on neighboring pixels, so that the segmented superpixel blocks were highly compatible with the actual plot shapes from a long-term period perspective. Then, a HyperGraph adjacency matrix was constructed, and a HyperGraph neural network (HGNN) was utilized to better learn the features of plots of the same crop that are distributed far from each other. The method fully utilizes the three dimensions of time, polarization and space information, which complement each other so as to effectively realize high-precision crop classification. The Sentinel-1 experimental results show that, under the optimal parameter settings, the classified accuracy of combined temporal superpixel scattering features using the HGNN was obviously improved, considering the near and far distance spatial correlations of crop types.https://www.mdpi.com/2072-4292/17/13/2304multi-temporaldual polarimetric SARcrop classificationsuperpixel segmentationHyperGraph neural network |
| spellingShingle | Qiang Yin Yuming Du Fangfang Li Yongsheng Zhou Fan Zhang Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization Remote Sensing multi-temporal dual polarimetric SAR crop classification superpixel segmentation HyperGraph neural network |
| title | Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization |
| title_full | Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization |
| title_fullStr | Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization |
| title_full_unstemmed | Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization |
| title_short | Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization |
| title_sort | multi temporal dual polarimetric sar crop classification based on spatial information comprehensive utilization |
| topic | multi-temporal dual polarimetric SAR crop classification superpixel segmentation HyperGraph neural network |
| url | https://www.mdpi.com/2072-4292/17/13/2304 |
| work_keys_str_mv | AT qiangyin multitemporaldualpolarimetricsarcropclassificationbasedonspatialinformationcomprehensiveutilization AT yumingdu multitemporaldualpolarimetricsarcropclassificationbasedonspatialinformationcomprehensiveutilization AT fangfangli multitemporaldualpolarimetricsarcropclassificationbasedonspatialinformationcomprehensiveutilization AT yongshengzhou multitemporaldualpolarimetricsarcropclassificationbasedonspatialinformationcomprehensiveutilization AT fanzhang multitemporaldualpolarimetricsarcropclassificationbasedonspatialinformationcomprehensiveutilization |