Landslide body identification and detection of high-resolution remote sensing image based on DETR

doi:10.13474/j.cnki.11-2246.2023.0129

Abstract

Abstract: Landslide disasters have attracted great attention because of their great destructiveness, and how to quickly and accurately detect landslides has become a major problem. Aiming at the problems of insufficient landslide detection dataset, low accuracy, and incomplete detection of landslide body, this paper combines the advantages of convolutional neural networks (CNN) and Transformer, and adopts the DETR network to realize the automatic detection of landslide body with Transformer as the main body. First of all, in order to solve the problem of insufficient data in the data set, the offline data enhancement method is used to achieve landslide data augmentation; Secondly, the DETR network structure using the encoder-decoder structure performs multi-scale training and prediction of the augmented dataset; Finally, the experimental results are quantitatively evaluated. Experimental results show that the average accuracy(AP)of landslide detection is 0.997,which can accurately identify and detect landslide bodies.In addition,the experimental results also verify that data enhancement can effectively improve the detection accuracy of landslide bodies in the DETR network.

Key words: landslide, object detection, convolutional neural network, DETR, attention mechanism

CLC Number:

P237

DU Yufeng, HUANG Liang, ZHAO Zilong, LI Guozhu. Landslide body identification and detection of high-resolution remote sensing image based on DETR[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 16-20.

References

[1] 张毅, 谭龙, 陈冠, 等. 基于面向对象分类法的高分辨率遥感滑坡信息提取[J]. 兰州大学学报(自然科学版), 2014, 50(5):745-750.
[2] 彭令, 徐素宁, 梅军军, 等. 地震滑坡高分辨率遥感影像识别[J]. 遥感学报, 2017, 21(4):509-518.
[3] 赵福军, 樊雅婧.深度卷积神经网络的遥感影像滑坡灾害识别[J].黑龙江科技大学学报, 2020, 30(5):556-561.
[4] CHENG Libo, LI Jia, DUAN Ping, et al. A small attentional YOLO model for landslide detection from satellite remote sensing images[J].Landslides, 2021, 18(8):2751-2765.
[5] JI Shunping, YU Dawen, SHEN Chaoyong, et al. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks[J].Landslides, 2020, 17(6):1337-1352.
[6] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[M]//Computer Vision-ECCV 2020. Cham:Springer International Publishing, 2020:213-229.
[7] 师芸, 石龙龙, 牛敏杰, 等.基于单阶段实例分割网络的黄土滑坡多任务自动识别[J].测绘通报, 2022(4):26-31.
[8] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE, 2016:770-778.
[9] 钱茹雪. 基于深度神经网络的花朵图像识别及分类[J]. 信息技术与信息化, 2022(3):210-213.
[10] HASSANIN M, ANWAR S, RADWAN I, et al. Visual attention methods in deep learning:an In-depth survey"[EB/OL].[2022-08-21].https://arxiv.org/abs/2204.07756.
[11] ZHOU Xingyi, WANG Dequan, KRÄHENBVHL P. Objects as points"[EB/OL].[2022-08-21]. https://arxiv.org/abs/1904.07850.
[12] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[13] TAN Mingxing, PANG Ruoming, LE Q V. EfficientDet:scalable and efficient object detection[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle:IEEE, 2020:10778-10787.
[14] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas:IEEE, 2016:779-788.
[15] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4:optimal speed and accuracy of object detection"[EB/OL].[2022-08-21]. https://arxiv.org/abs/2004.10934.