基于Faster R-CNN改进算法的遥感技术及其在地质灾害监测中的应用研究

doi:10.13686/j.cnki.dzyzy.2023.06.014

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Abstract To reduce the damage caused by geological disasters to human beings, accurate monitoring of geological information in natural environment is of great significance. Based on the correlation between geological hazards and remote sensing technology, the study introduces the faster regions with convolutional neural network (Faster R-CNN) algorithm, further utilizes feature extraction network, modifies the training method and optimizes the algorithm, so that it can be applied to remote sensing technology to improve the speed and accuracy of geological hazards detection. Finally, the PASCAL VOC and COCO datasets are used for the experimental evaluation and demonstration of the proposed method model. The results show that when the GIOU value is 0.6, the detection accuracy reaches the best. In the algorithm comparison, the AP@0.5 value of the improved Faster R-CNN algorithm designed in the study reaches 59, proving that the algorithm is satisfactory in both speed and accuracy, and achieves the expected goal of target detection. Moreover, the Faster R-CNN algorithm can be effectively applied to satellite remote sensing technology to quickly detect geological information in natural environment and make early warning measures by judging its attribute transformation, thus reducing losses caused by geological changes.

Key words： satellite remote sensing geological hazard environmental monitoring target detection Faster R-CNN algorithm

Received: 27 December 2021

PACS:

TP753

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	Articles by authors
	CUI Na
	LU Xiao-hong
	WANG Yan
	LI Fei
	SONG Shan
	GUO Qing-ni
	ZHOU Shao-wei
	SHI Li-yan

Cite this article:

CUI Na,LU Xiao-hong,WANG Yan等. REMOTE SENSING TECHNOLOGY BASED ON IMPROVED FASTER R-CNN ALGORITHM AND ITS APPLICATION IN GEOLOGICAL DISASTER MONITORING[J]. GEOLOGY AND RESOURCES, 2023, 32(6): 772-778.

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http://manu25.magtech.com.cn/Jweb_dzyzy/EN/10.13686/j.cnki.dzyzy.2023.06.014 OR http://manu25.magtech.com.cn/Jweb_dzyzy/EN/Y2023/V32/I6/772

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