Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images
-
摘要: 利用遥感手段实现尾矿库空间范围的快速识别对我国尾矿库监测监管具有重要意义。以U-Net网络框架为基础,提出了基于深度学习的尾矿库空间范围遥感智能识别方法,利用国产高分六号影像在云南省红河哈尼族彝族自治州开展了应用验证。结果表明,该方法对尾矿库空间范围识别的精确率(Precision)、召回率(Recall)、F1-score值分别达到0.874,0.843和0.858,显著优于随机森林、支持向量机、最大似然法; 尾矿库空间范围识别的耗时与上述3种方法保持相同的数量级水平。该方法在全国尾矿库空间范围变化的遥感快速监测中具有广阔的应用前景。Abstract: It is of great significance for the monitoring and supervision of tailing ponds in China to realize the rapid recognition of the spatial scopes of tailing ponds using the remote sensing technique. Based on the U-Net framework, this paper proposes a deep learning-based intelligent recognition method of the spatial ranges of tailing ponds using the remote sensing technique. The method proposed was verified in Honghe Hani and Yi Autonomous Prefecture in Yunnan Province using Chinese GF-6 satellite images. The results show that the precision, recall rate, and F1-score of the method were 0.874, 0.843, and 0.858, respectively, which were significantly better than those obtained using the methods of random forest, support vector machine, and maximum likelihood. Furthermore, the time consumption of the new method kept the same order of magnitude as that of the three methods. Therefore, the method proposed in this study has a broad application prospect in the rapid monitoring of the spatial scopes of tailing ponds in China.
-
Key words:
- deep neural network /
- GF-6 satellite /
- tailing pond /
- recognition based on remote sensing images /
-
-
[1] 沃廷枢, 汪贻水, 肖垂斌, 等. 尾矿库手册[M]. 北京: 冶金工业出版社, 2013:1-13.
[2] Wo T S, Wang Y S, Xiao C B, et al. Manual of tailings pond[M]. Beijing: Metallurgical Industry Press, 2013:1-13.
[3] Rotta S, Henrique L, Alcantara E, et al. The 2019 Brumadinho tailings dam collapse:Possible cause and impacts of the worst human and environmental disaster in Brazil[J]. International Journal of Applied Earth Observation and Geoinformation, 2020,90:102119.
[4] 郝利娜, 张志, 何文熹, 等. 鄂东南尾矿库高分辨率遥感图像识别因子研究[J]. 国土资源遥感, 2012,24(3):154-158.doi: 10.6046/gtzyyg.2012.03.27.
[5] Hao L N, Zhang Z, He W X, et al. Tailings reservoir recognition factors of the high resolution remote sensing image in southeastern of Hubei[J]. Remote Sensing for Land and Resources, 2012,24(3):154-158.doi: 10.6046/gtzyyg.2012.03.27.
[6] Ma B D, Chen Y T, Zhang S, et al. Remote sensing extraction method of tailings ponds in ultra-low-grade iron mining area based on spectral characteristics and texture entropy[J]. Entropy, 2018,20(5):345.
[7] Hao L N, Zhang Z, Yang X X. Mine tailing extraction indexes and model using remote-sensing images in southeast Hubei Province[J]. Environmental Earth Sciences, 2019,78(15):493.
[8] 曹兰杰, 吴兵, 汪金花, 等. 面向对象的高分一号铁尾矿遥感信息提取与分析[J]. 测绘与空间地理信息, 2019,42(4):98-101.
[9] Cao L J, Wu B, Wang J H, et al. Object-oriented information extraction and analysis of the iron tailings with GF-1 remote sensing image[J]. Geomatics & Spatial Information Technology, 2019,42(4):98-101.
[10] Fuentes M, Millard K, Laurin E. Big geospatial data analysis for Canada’s air pollutant emissions inventory (APEI):Using google earth engine to estimate particulate matter from exposed mine disturbance areas[J]. Giscience & Remote Sensing, 2019,57(2):245-257.
[11] 王立廷. 支持向量机结合面向对象提取尾矿库的应用研究[D]. 北京:中国地质大学(北京), 2018.
[12] Wang L T. Research on application of support vector machine combined with object oriented method for tailing pond extraction[D]. Beijing:China University of Geosciences (Beijing), 2018.
[13] Orimoloye I R, Ololade O O. Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology[J]. International Journal of Environmental Science and Technology, 2020,17(11):4465-4480.
[14] 闫凯, 沈汀, 陈正超, 等. 基于深度学习的SSD模型尾矿库自动提取[J]. 中国科学院大学学报, 2020,37(3):360-367.
[15] Yan K, Shen T, Chen Z C, et al. Automatic extraction of tailing pond based on SSD of deep learning[J]. Journal of University of Chinese Academy of Sciences, 2020,37(3):360-367.
[16] Li Q T, Chen Z C, Zhang B, et al. Detection of tailings dams using high-resolution satellite imagery and a single shot multibox detector in the Jing-Jin-Ji region,China[J]. Remote Sensing, 2020,12(16).
[17] 王建伟, 刘红军, 郭科. 基于粗糙集理论的矿产资源地质调查综合评价模型及其应用——以滇东南地区为例[J]. 资源科学, 2014,36(8):1608-1617.
[18] Wang J W, Liu H J, Guo K. Mineral resource geological survey evaluation model based on a rough set theory:A case study in southeastern Yunnan[J]. Resources Science, 2014,36(8):1608-1617.
[19] 潘含江. 我国典型金属矿山尾矿地球化学特征及资源环境评价[D]. 北京:中国地质大学(北京), 2019.
[20] Pan H J. Geochemical characteristics and resource environment assessment of tailings from typical metal mines in China[D]. Beijing:China University of Geosciences(Beijing), 2019.
[21] Yang A X, Zhong B, Hu L F, et al. Radiometric cross-calibration of the wide field view camera onboard GaoFen-6 in multispectral bands[J]. Remote Sensing, 2020,12(6):1037.
[22] 杨可明. 遥感原理与应用[M]. 北京: 中国矿业大学出版社, 2016:231-234.
[23] Yang K M. Remote sensing principle and applications[M]. Beijing: China University of Mining and Technology Press, 2016:231-234.
[24] Ronneberger O, Fischer P, Brox T. U-Net:Convolutional networks for biomedical image segmentation[C]//Medical image Computing and Computer-Assisted Intervention, 2015:234-241.
[25] Deepsense A. Deep learning for satellite imagery via image segmentation[EB/OL].(2017-4-12)[2021-01-12]. https://deepsense.io/deep-learning-for-satellite-imagery-via-image-segmentation/.
[26] Zhu X L, Cheng Z Y, Wang S, et al. Coronary angiography image segmentation based on PSPNet[J]. Computer Methods and Programs in Biomedicine, 2020,200(4):105897.
[27] Diaz V, Jairo J, Vlaminck M, et al. Solar panel detection within complex backgrounds using thermal images acquired by UAVs[J]. Sensors, 2020,20(21):6219.
[28] Zhang L X, Shen J K, Zhu B J. A research on an improved Unet-based concrete crack detection algorithm[J]. Structural Health Monitoring-An International Journal, 2020: 1475921720940068.
-
计量
- 文章访问数: 841
- PDF下载数: 43
- 施引文献: 0
下载: