STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images
Remote sensing-based classification of crops is the foundation for the monitoring of food production and management. A range of remote sensing images, encompassing spatial, spectral, and temporal dimensions, has facilitated the classification of crops. However, prevailing methods for crop classifica...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10848201/ |
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| _version_ | 1825207011737862144 |
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| author | Wei Wu Yapeng Liu Kun Li Haiping Yang Liao Yang Zuohui Chen |
| author_facet | Wei Wu Yapeng Liu Kun Li Haiping Yang Liao Yang Zuohui Chen |
| author_sort | Wei Wu |
| collection | DOAJ |
| description | Remote sensing-based classification of crops is the foundation for the monitoring of food production and management. A range of remote sensing images, encompassing spatial, spectral, and temporal dimensions, has facilitated the classification of crops. However, prevailing methods for crop classification via remote sensing focus on either temporal or spatial features of images. These unimodal methods often encounter challenges posed by noise interference in real-world scenarios, and may struggle to discriminate between crops with similar spectral signatures, thereby leading to misclassification over extensive areas. To address the issue, we propose a novel approach termed spatiotemporal fusion-based crop classification network (STFCropNet), which integrates high-resolution (HR) images with medium-resolution time-series (TS) images. STFCropNet consists of a temporal branch, which captures seasonal spectral variations and coarse-grained spatial information from TS data, and a spatial branch that extracts geometric details and multiscale spatial features from HR images. By integrating features from both branches, STFCropNet achieves fine-grained crop classification while effectively reducing salt and pepper noise. We evaluate STFCropNet in two study areas of China with diverse topographic features. Experimental results demonstrate that STFCropNet outperforms state-of-the-art models in both study areas. STFCropNet achieves an overall accuracy of 83.2% and 90.6%, representing improvements of 3.6% and 4.1%, respectively, compared to the second-best baseline model. We release our code at. |
| format | Article |
| id | doaj-art-3ee9cdde193e44be8f3ad50c38ee4fc5 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-3ee9cdde193e44be8f3ad50c38ee4fc52025-02-07T00:00:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184736475010.1109/JSTARS.2025.353188610848201STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing ImagesWei Wu0https://orcid.org/0000-0002-1269-9045Yapeng Liu1Kun Li2Haiping Yang3Liao Yang4Zuohui Chen5https://orcid.org/0000-0003-1806-6676College of Geoinformatics, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, ChinaCollege of Geoinformatics, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, ChinaRemote sensing-based classification of crops is the foundation for the monitoring of food production and management. A range of remote sensing images, encompassing spatial, spectral, and temporal dimensions, has facilitated the classification of crops. However, prevailing methods for crop classification via remote sensing focus on either temporal or spatial features of images. These unimodal methods often encounter challenges posed by noise interference in real-world scenarios, and may struggle to discriminate between crops with similar spectral signatures, thereby leading to misclassification over extensive areas. To address the issue, we propose a novel approach termed spatiotemporal fusion-based crop classification network (STFCropNet), which integrates high-resolution (HR) images with medium-resolution time-series (TS) images. STFCropNet consists of a temporal branch, which captures seasonal spectral variations and coarse-grained spatial information from TS data, and a spatial branch that extracts geometric details and multiscale spatial features from HR images. By integrating features from both branches, STFCropNet achieves fine-grained crop classification while effectively reducing salt and pepper noise. We evaluate STFCropNet in two study areas of China with diverse topographic features. Experimental results demonstrate that STFCropNet outperforms state-of-the-art models in both study areas. STFCropNet achieves an overall accuracy of 83.2% and 90.6%, representing improvements of 3.6% and 4.1%, respectively, compared to the second-best baseline model. We release our code at.https://ieeexplore.ieee.org/document/10848201/Fine-grained crop classificationhigh-resolution (HR) imagesspatiotemporal fusiontime-series (TS) images |
| spellingShingle | Wei Wu Yapeng Liu Kun Li Haiping Yang Liao Yang Zuohui Chen STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Fine-grained crop classification high-resolution (HR) images spatiotemporal fusion time-series (TS) images |
| title | STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images |
| title_full | STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images |
| title_fullStr | STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images |
| title_full_unstemmed | STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images |
| title_short | STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images |
| title_sort | stfcropnet a spatiotemporal fusion network for crop classification in multiresolution remote sensing images |
| topic | Fine-grained crop classification high-resolution (HR) images spatiotemporal fusion time-series (TS) images |
| url | https://ieeexplore.ieee.org/document/10848201/ |
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