Information extraction of landslides based on high-resolution remote sensing images and an improved U-Net model: A case study of Wenchuan, Sichuan
BAI Shi1(), TANG Panpan1(), MIAO Zhao2, JIN Caifeng3, ZHAO Bo1, WAN Haoming1
1. Research Center of Big Data Technology, Nanhu Laboratory, Jiaxing 314002, China 2. Institute of Exploration Technology Chinese Academy of Geological Sciences, Chengdu 611734, China 3. School of Architectural Engineering, Jiaxing Nanhu University, Jiaxing 314001, China
Rapid identification and detection of landslides can both meet the requirement of timely responses to disasters and hold great significance for loss assessment and rescue post-disaster. This study proposed a deep learning-based automatic information extraction method for landslides to improve their detection accuracy. Specifically, the model input of this method includes the remote sensing images of the target areas, data from digital elevation models, and variation characteristics extracted using robust change vector analysis (RCVA). Furthermore, a U-Net model integrating dense upsampling and asymmetric convolution is designed to improve the identification accuracy. Taking Wenchuan, Sichuan Province as the study area, this study designed experiments to test the pixel-level image segmentation accuracy of landslides using different data combinations and methods. The results indicate that the improved U-Net model proposed in the study can produce the optimal image segmentation results of landslides.
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BAI Shi, TANG Panpan, MIAO Zhao, JIN Caifeng, ZHAO Bo, WAN Haoming. Information extraction of landslides based on high-resolution remote sensing images and an improved U-Net model: A case study of Wenchuan, Sichuan. Remote Sensing for Natural Resources, 2024, 36(3): 96-107.
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