Hanjie Lin, Li Li, Yue Qiang, Xinlong Xu, Siyu Liang, Tao Chen, Wenjun Yang, Yi Zhang
{"title":"高精度航空图像中的滑坡识别和检测方法:渐进式 CBAM-U-net 模型","authors":"Hanjie Lin, Li Li, Yue Qiang, Xinlong Xu, Siyu Liang, Tao Chen, Wenjun Yang, Yi Zhang","doi":"10.1007/s12145-024-01465-6","DOIUrl":null,"url":null,"abstract":"<p>Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A method for landslide identification and detection in high-precision aerial imagery: progressive CBAM-U-net model\",\"authors\":\"Hanjie Lin, Li Li, Yue Qiang, Xinlong Xu, Siyu Liang, Tao Chen, Wenjun Yang, Yi Zhang\",\"doi\":\"10.1007/s12145-024-01465-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01465-6\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01465-6","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A method for landslide identification and detection in high-precision aerial imagery: progressive CBAM-U-net model
Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection.
期刊介绍:
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.