| 148 | 2 | 212 |
| 下载次数 | 被引频次 | 阅读次数 |
利用遥感影像获取准确的滑坡范围信息对分析滑坡成因和制定防灾减灾规划具有重要意义。利用遥感影像提取滑坡时存在边界模糊、内部空洞、整体实例不连续等问题，因此基于深度学习方法提出了一种滑坡提取网络DAR-Net。该网络在U-Net的基础上引入双空间注意力模块和由粗到细的边界细化模块，分别用于捕获全局局部特征和细化边界，以获得更加准确的滑坡识别结果。实验结果表明，与多种语义分割算法相比，该算法在开源数据集上获得了最优的性能。
Abstract：Rapid and accurate landslide extraction from remote sensing images is of great significance for disaster prevention, reduction, andemergency response. Existing remote sensing image landslide extraction algorithms have problems such as blurred boundaries, internal holes,and overall instance discontinuity. In response to these issues, we proposed a landslide extraction network called DAR-Net. This networkintroduces the dual spatial attention module and coarse-to-fine boundary refinement module on the basis of U-Net, which are respectively used tocapture global and local features and refine boundaries. Compared with the semantic segmentation algorithms, this algorithm produces the bestperformance on the open source dataset.
[1] Pradhan B ,Mezaal M R. Data Mining-aided Automatic Landslide Detection Using Airborne Laser Scanning Data in Densely Forested Tropical Areas[J]. Korean Journal of Remote Sensing,2018,34(1):45-74
[2]刘鹏，邹崇尧，何雯，等.基于无人机遥感的滑坡几何参数测量方法研究[J].地理空间信息，2023,21(1):123-126
[3] Cheng L,Li J,Duan P,et al. A Small Attentional YOLO Model for Landslide Detection from Satellite Remote Sensing Images[J]. Landslides,2021,18(8):2 751-2 765
[4]马妍，古丽米拉·克孜尔别克.图像语义分割方法在高分辨率遥感影像解译中的研究综述[J].计算机科学与探索，2023,17(7):1 526-1 548
[5]赵会芹，于博，陈方，等.基于高分辨率卫星遥感影像滑坡提取方法研究现状[J].遥感技术与应用，2023,38(1):108-115
[6] Blaschke T. Object Based Image Analysis for Remote Sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16
[7] Yang X ,Chen L. Using Multi-temporal Remote Sensor Imagery to Detect Earthquake-triggered Landslides[J].International Journal of Applied Earth Observation and Geoinformation,2010,12(6):487-495
[8] Fang C,Fan X,Zhong H,et al. A Novel Historical Landslide Detection Approach Based on LiDAR and Lightweight Attention U-Net[J]. Remote Sensing,2022,14(17):4 357
[9] Ghorbanzadeh O,Meena S R,Blaschke T,et al. UAV-based Slope Failure Detection Using Deep-learning Convolutional Neural Networks[J]. Remote Sensing,2019,11(17):2 046
[10] Bhuyan K,Meena S R,Nava L,et al. Mapping Landslides Through aTemporal Lens:An InsightToward Multi-temporal Landslide Mapping Using the U-Net Deep Learning Model[J]. GIScience and Remote Sensing ,2023 ,60(1):2 182 057
[11] Ghorbanzadeh O ,Xu Y ,Ghamisi P ,et al. Landslide4Sense:Reference Benchmark Data and Deep Learning Models for Landslide Detection[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:5 633 017
[12] Simonyan K,Zisserman A. Very Deep Convolutional Networks for Large-scale Image Recognition[J]. ar Xiv preprint ar Xiv:1409.1556,2014
[13] Zhang R,Wan Z,Zhang Q,et al. DSAT-Net:Dual Spatial Attention Transformer for Building Extraction from Aerial Images[J]. IEEE Geoscience and Remote Sensing Letters,2023,20:6 008 405
[14] Guo H,Du B,Zhang L,et al. A Coarse-to-fine Boundary Refinement Network for Building Footprint Extraction from Remote Sensing Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2022,183:240-252
[15] Guo M H,Lu C Z,Hou Q,et al. Segnext:Rethinking Convolutional Attention Design for Semantic Segmentation[J].Advances in Neural Information Processing Systems,2022,35:1 140-1 156
[16] Ronneberger O,Fischer P,Brox T. U-Net:Convolutional Networks for Biomedical Image Segmentation[C]//Proceedings of Medical Image Computing and Computer-assisted Intervention-MICCAI,Munich,Germany,2015:234-241
[17] Chen L C,Zhu Y,Papandreou G,et al. Encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:801-818
[18] Yu C,Gao C,Wang J,et al. Bisenet v2:Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation[J]. International Journal of Computer Vision,2021,129:3 051-3 068
[19] Fu J,Liu J,Tian H,et al. Dual Attention Network for Scene Segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:3 146-3 154
[20] Huang Z ,Wang X ,Huang L ,et al. Ccnet:Criss-cross Attention for Semantic Segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:603-612
基本信息:
中图分类号:P642.22;P237
引用信息:
[1]严炳杨,冯志荣,鲍灵辉,等.DAR-Net：双空间注意力与边界细化的遥感影像滑坡提取网络[J].地理空间信息,2025,23(05):1-4+38.
基金信息:
西成铁路客运专线陕西有限责任公司科技研究重点开发计划资助项目(西康高铁合(2021)21号); 四川省科技计划资助项目(2023NSFSC0247)
2025-05-26
2025-05-26