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

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hanjie Lin, Li Li, Yue Qiang, Xinlong Xu, Siyu Liang, Tao Chen, Wenjun Yang, Yi Zhang
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引用次数: 0

摘要

快速识别和检测滑坡对灾害损失评估和灾后救援具有重要意义。然而,用于快速识别和检测滑坡的 U 网存在语义空白和空间信息丢失的问题。为此,本文提出了一种带有渐进式卷积块注意模块(CBAM-U-net)的 U-net,用于从高精度航空图像中识别和提取滑坡边界。首先,收集了 109 幅高精度滑坡航空影像,并通过数据增强扩展了原始数据库,以增强模型的泛化能力。随后,通过在 U-net 中的每个下采样过程中引入空间关注模块和通道关注模块,构建了 CBAM-U-net 模型。同时,将 U-net、FCN 和 DeepLabv3 + 作为对比模型。最后,使用 6 个评价指标来综合评估模型在滑坡识别和分割方面的能力。结果表明,与其他模型相比,CBAM-U-net 的识别和分割精度更高,平均行正确率、骰子系数、全局正确率、IoU 和平均 IoU 的最佳值分别为 98.3、0.877、95、88.5 和 90.2。U-net、DeepLab V3 + 和 FCN 容易将裸露地面和道路与滑坡混淆。所提出的方法可以改善 U-net 中语义空白和空间信息丢失的问题,在识别和分割高精度滑坡图像时具有较好的准确性和鲁棒性,可为滑坡快速识别与检测的研究提供一定的参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A method for landslide identification and detection in high-precision aerial imagery: progressive CBAM-U-net model

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.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
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.
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