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• W4321609022 abstract "Landslide mapping (LM) from bitemporal remote sensing images is essential for disaster prevention and mitigation. Bitemporal change detection technology has been applied for LM, however, there remains room for improvement in its accuracy and automation. In this paper, a multi-level feature enhancement network (MFENet) is proposed for LM based on modules built in convolutional neural network (CNN) like CNN-Attention. MFENet mainly consists of three modules: post-event feature enhancement module (PFEM), bi-feature difference enhancement module (BFDEM) and flow direction calibration module (FDCM). Specifically, the main role of PFEM selectively fuses post-event multi-layer features to provide discriminative post-event features. BFDEM fuses the multi-layer differences of both pre-event and post-event features to generate high-quality change detection features, sufficiently powerful to distinguish foreground from background. FDCM uses a digital elevation model (DEM) to calibrate the flow direction of each pixel of the landslide detection results to complete the LM task. Experiments were conducted to test the effectiveness of MFENet on two real-world regions, Lantau Island and Sharp Peak, Hong Kong, in which landslides occurred after rainstorms. Compared with other state-of-the-art general change detection methods and landslide-specific change detection methods, the proposed method outperforms on all metrics with its intersection over union (IoU) reaching 87.23%. The availability of additional features and the generalization performance of MFENet are demonstrated experimentally. It is anticipated that the proposed network will further contribute to disaster prevention and mitigation." @default.
• W4321609022 created "2023-02-24" @default.
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• W4321609022 date "2023-01-01" @default.
• W4321609022 modified "2023-09-30" @default.
• W4321609022 title "Landslide Mapping Using Multilevel-Feature-Enhancement Change Detection Network" @default.
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• W4321609022 doi "https://doi.org/10.1109/jstars.2023.3245062" @default.
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