STFNet: A Spatiotemporal Fusion Network for Forest Change Detection Using Multi-Source Satellite Images
Forest resources have important ecological and environmental values, and monitoring forest changes using remote sensing images is essential for resource management and ecological protection. However, current forest change detection methods fail to simultaneously integrate fine spatial information wi...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/24/4736 |
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| Summary: | Forest resources have important ecological and environmental values, and monitoring forest changes using remote sensing images is essential for resource management and ecological protection. However, current forest change detection methods fail to simultaneously integrate fine spatial information with temporal dynamic data, making them susceptible to pseudo changes induced by seasonal factors. In this paper, we propose a forest change detection method called STFNet that integrates multi-source spatiotemporal information. By combining fine spatial details of high-resolution images with dynamic information from time-series images, STFNet enhances the accuracy of forest change detection, alleviating the problem of information fusion difficulties caused by inconsistent granularity in spatiotemporal spectral features from different sources. In STFNet, we propose a cross-attention-based temporal differential feature fusion module (CATFF) to capture spatiotemporal dependencies within time-series images and a multiresolution contextual differential feature fusion module (MCDF) to achieve efficient spatial contexture fusion across multiresolution images. To validate our method, we conduct experiments using Gaofen and Sentinel-2 satellite images. Experimental results demonstrate that STFNet achieves excellent performance with an F1-score of 87.65%, outperforming state-of-the-art methods by at least 2.02%. Our ablation study further confirms the effectiveness of our method in leveraging time-series information to detect forest changes and suppress seasonal interference. |
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| ISSN: | 2072-4292 |