REMOTE SENSING TECHNOLOGY BASED ON IMPROVED FASTER R-CNN ALGORITHM AND ITS APPLICATION IN GEOLOGICAL DISASTER MONITORING

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

doi:10.13686/j.cnki.dzyzy.2023.06.014

2023 Vol. 32, No. 6

Article Contents

Article navigation > Geology and Resources > 2023 > 32(6): 772-778

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CUI Na, LU Xiao-hong, WANG Yan, LI Fei, SONG Shan, GUO Qing-ni, ZHOU Shao-wei, SHI Li-yan. 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. doi: 10.13686/j.cnki.dzyzy.2023.06.014

Citation:

CUI Na, LU Xiao-hong, WANG Yan, LI Fei, SONG Shan, GUO Qing-ni, ZHOU Shao-wei, SHI Li-yan. 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. doi: 10.13686/j.cnki.dzyzy.2023.06.014

REMOTE SENSING TECHNOLOGY BASED ON IMPROVED FASTER R-CNN ALGORITHM AND ITS APPLICATION IN GEOLOGICAL DISASTER MONITORING

1.
Shaanxi Geological Science and Technology Center, Xi'an 710054, China
2.
Shaanxi Academician and Expert Workstation, Xi'an 710054, China
3.
Shaanxi Social Science Education Base, Xi'an 710054, China
4.
Beijing Research Institute of Smart Water, Beijing 100036, China

Received Date: 27 December 2021

Revised Date: 08 October 2022

Publish Date: 25 December 2023

Abstract

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.
- satellite remote sensing /
- geological hazard /
- environmental monitoring /
- target detection /
- Faster R-CNN algorithm

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References

[1]	Meng R H, Rice S G, Wang J, et al. A fusion steganographic algorithm based on faster R-CNN[J]. Computers, Materials & Continua, 2018, 55(1): 1-16. Google Scholar
[2]	葛大庆. 地质灾害早期识别与监测预警中的综合遥感应用[J]. 城市与减灾, 2018(6): 53-60. Google Scholar Ge D Q. Comprehensive application of remote sensing in early identification, monitoring and early warning in geological disasters[J]. City and Disaster Reduction, 2018(6): 53-60. Google Scholar
[3]	毕瑞, 甘淑, 李绕波, 等. 面向东川复杂山地泥石流沟谷三维地形建模及特征分析的无人机遥感探测应用研究[J]. 中国地质灾害与防治学报, 2021, 32(3): 91-100. Google Scholar Bi R, Gan S, Li R B, et al. Application research of unmanned aerial vehicle remote sensing detection for 3D terrain modelling and feature analysis of debris flow gullies in complex mountainous area of Dongchuan[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(3): 91-100. Google Scholar
[4]	Lian X G, Li Z J, Yuan H Y, et al. Rapid identification of landslide, collapse and crack based on low-altitude remote sensing image of UAV[J]. Journal of Mountain Science, 2020, 17(12): 2915-2928. doi: 10.1007/s11629-020-6080-9 CrossRef Google Scholar
[5]	Athanassas C D. Muography for geological hazard assessment in the South Aegean active volcanic arc (SAAVA)[J]. Mediterranean Geoscience Reviews, 2020, 2(2): 233-246. doi: 10.1007/s42990-020-00020-x CrossRef Google Scholar
[6]	Lai F, Shao Q F, Lin Y, et al. A method for the hazard assessment of regional geological disasters: A case study of the Panxi area, China[J]. Journal of Spatial Science, 2021, 66(1): 143-162. doi: 10.1080/14498596.2019.1606741 CrossRef Google Scholar
[7]	Qi L, Li B Y, Chen L K, et al. Ship target detection algorithm based on improved faster R-CNN[J]. Electronics, 2019, 8(9): 959. doi: 10.3390/electronics8090959 CrossRef Google Scholar
[8]	Gao M Y, Du Y J, Yang Y X, et al. Adaptive anchor box mechanism to improve the accuracy in the object detection system[J]. Multimedia Tools and Applications, 2019, 78(19): 27383-27402. doi: 10.1007/s11042-019-07858-w CrossRef Google Scholar
[9]	Liu P, Wei Y M, Wang Q J, et al. A research on landslides automatic extraction model based on the improved mask R-CNN[J]. ISPRS International Journal of Geo-Information, 2021, 10(3): 168. doi: 10.3390/ijgi10030168 CrossRef Google Scholar
[10]	Vabalas A, Gowen E, Poliakoff E, et al. Machine learning algorithm validation with a limited sample size[J]. PLoS One, 2019, 14(11): e0224365. doi: 10.1371/journal.pone.0224365 CrossRef Google Scholar
[11]	Krinitskiy M, Aleksandrova M, Verezemskaya P, et al. On the generalization ability of data-driven models in the problem of total cloud cover retrieval[J]. Remote Sensing, 2021, 13(2): 326. doi: 10.3390/rs13020326 CrossRef Google Scholar
[12]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报·信息科学版, 2019, 44(7): 957-966. Google Scholar Xu Q, Dong X J, Li W L. Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 957-966. Google Scholar
[13]	Siregar S P, Wanto A. Analysis of artificial neural network accuracy using backpropagation algorithm in predicting process (forecasting)[J]. IJISTECH (International Journal of Information System and Technology), 2017, 1(1): 34-42. Google Scholar
[14]	Xu Q H, Yuan S F, Huang T X. Multi-dimensional uniform initialization Gaussian mixture model for spar crack quantification under uncertainty[J]. Sensors, 2021, 21(4): 1283. Google Scholar
[15]	张春晓, 鲍云飞, 马中祺, 等. 基于卷积神经网络的光学遥感目标检测研究进展[J]. 航天返回与遥感, 2020, 41(6): 45-55. Google Scholar Zhang C X, Bao Y F, Ma Z Q, et al. Research progress on optical remote sensing object detection based on CNN[J]. Spacecraft Recovery & Remote Sensing, 2020, 41(6): 45-55. Google Scholar
[16]	Tanatipuknon A, Aimmanee P, Watanabe Y, et al. Study on combining two faster R-CNN models for landslide detection with a classification decision tree to improve the detection performance[J]. Journal of Disaster Research, 2021, 16(4): 588-595. Google Scholar
[17]	Mahmood T, Arsalan M, Owais M, et al. Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs[J]. Journal of Clinical Medicine, 2020, 9(3): 749. Google Scholar
[18]	Zhang N, Feng Y R, Lee E J. Activity object detection based on improved faster R-CNN[J]. Journal of Korea Multimedia Society, 2021, 24(3): 416-422. Google Scholar
[19]	Singh S, Ahuja U, Kumar M, et al. Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment[J]. Multimedia Tools and Applications, 2021, 80(13): 19753-19768. Google Scholar
[20]	Chen K, Tao W B. Learning linear regression via single-convolutional layer for visual object tracking[J]. IEEE Transactions on Multimedia, 2019, 21(1): 86-97. Google Scholar
[21]	刘小波, 刘鹏, 蔡之华, 等. 基于深度学习的光学遥感图像目标检测研究进展[J]. 自动化学报, 2021, 47(9): 2078-2089. Google Scholar Liu X B, Liu P, Cai Z H, et al. Research progress of optical remote sensing image object detection based on deep learning[J]. Acta Automatica Sinica, 2021, 47(9): 2078-2089. Google Scholar
[22]	Zhao K, Wang Y N, Zhu Q, et al. Intelligent detection of parcels based on improved faster R-CNN[J]. Applied Sciences, 2022, 12(14): 7158. Google Scholar
[23]	Martín-Doñas J M, Gomez A M, Gonzalez J A, et al. A deep learning loss function based on the perceptual evaluation of the speech quality[J]. IEEE Signal Processing Letters, 2018, 25(11): 1680-1684. Google Scholar
[24]	欧攀, 张正, 路奎, 等. 基于卷积神经网络的遥感图像目标检测[J]. 激光与光电子学进展, 2019, 56(5): 051002. Google Scholar Ou P, Zhang Z, Lu K, et al. Object detection of remote sensing images based on convolutional neural networks[J]. Laser & Optoelectronics Progress, 2019, 56(5): 051002. Google Scholar