Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province

doi:10.6046/zrzyyg.2021027

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Abstract

The recognition and extraction of mine tailing information serve as an important step in the dynamic monitoring of the mine environment. The classification of surface features using medium-low spatial resolution images is mostly conducted based on spectral information. However, some roads and tailings have similar spectral reflectance due to the special environment in mining areas. As a result, it is liable to misclassify tailings as roads in the surface feature classification based on spectral information only, which affects the structural integrity and area statistics of tailing ponds. Given this, this paper comprehensively analyzes the spectral, shape-related, and texture characteristics of iron mine tailings in the Qianxi area, Hebei Province based on high spatial resolution images obtained from the Beijing-2 satellite and proposes an object-oriented classification method based on multiple features. The steps of the method are as follows. Firstly, perform multi-scale segmentation of Beijing-2 images and the reflectance and take the spectral differences of surface features in each band as the spectral characteristic values of surface features. Secondly, extract the values of length-to-width ratio of objects using a covariance matrix and object boundaries and take them as the characteristic values of surface feature shapes. Then, calculate the gray-level co-occurrence matrix using principal component bands, and select the contrast, correlation, and entropy values that can effectively distinguish the texture characteristics between tailings and other surface features as the texture characteristic values of remote sensing images. Finally, conduct object-oriented classification and precision assessment using the nearest neighbor method according to the characteristic information of surface features. The results indicate that the object-oriented classification method can effectively avoid the misclassification of the roads in tailing ponds and thus provide a research basis for the implementation of large-scope and high-precision identification and dynamic monitoring of mine tailings.

Keywords iron tailings feature selection image segmentation object-oriented classification

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TP79

Issue Date: 23 December 2021

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	Yinglin FAN
	Debo LOU
	Changqing ZHANG
	Yingjuan WEI
	Fudong JIA

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Yinglin FAN,Debo LOU,Changqing ZHANG, et al. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021027 OR https://www.gtzyyg.com/EN/Y2021/V33/I4/153

Fig.1 Location of study area

Fig.2 Fusion of panchromatic and multispectral bands

Tab.1 Beijing-2 spectrum information

Fig.3 Spectral curve in the study area

Fig.4 Length-width ratio of ground features

Fig.5 Extraction results of feature texture

Fig.6 The heterogeneity factor that determines the effect of multi-scale segmentation

Fig.7 Segmentation results with a compactness factor of 0.5

Fig.8 Segmentation results with a shape factor of 0.4

Fig.9 ROC-LV graph

Fig.10 The segmentation scale was 100, the shape factor was 0.4, and the compactness factor was 0.5

Fig.11 The result graph of object-oriented classification and pixel - based classification

Fig.12 Graph of iron tailings information extraction by object-oriented method and pixel - based method

Tab.2 Object-oriented classification and pixel-based classification accuracy evaluation

[1]	Bian Z F, Miao X X, Lei S G, et al. The challenges of reusing mining and mineral-processing wastes[J]. Science, 2012,337(6095):702-703. doi: 10.1126/science.1224757 url: https://www.science.org/doi/10.1126/science.1224757
[2]	王海军, 王伊杰, 李文超, 等. 全国矿产资源节约与综合利用报告(2019)[J]. 中国国土资源经济, 2020,33(2):2.
[2]	Wang H J, Wang Y J, Li W C, et al. The report of mineral resources saving and comprehensive utilization in China(2019)[J]. Natural Resource Economics of China, 2020,33(2):2.
[3]	Liu R Z, Liu J, Zhang Z J, et al. Accidental water pollution risk analysis of mine tailings ponds in guanting reservoir watershed,Zhangjiakou City,China[J]. International Journal of Environmental Research and Public Health, 2015,12(12):15269-15284. doi: 10.3390/ijerph121214983 url: http://www.mdpi.com/1660-4601/12/12/14983
[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. doi: 10.6046/gtzyyg.2012.03.27
[4]	Hao L N, Zhang Z, He W X, et al. Tailings reservoir recognition factors of the high resolution remote sensing image in southeastern Hubei[J]. Remote Sensing for Land and Resources, 2012,24(3):154-158.doi: 10.6046/gtzyyg.2012.03.27. doi: 10.6046/gtzyyg.2012.03.27
[5]	汪金花, 白洋, 刘雨青. 高分辨率铁尾矿遥感光谱特征分析与信息提取[J]. 国土资源科技管理, 2015,32(1):79-85.
[5]	Wang J H, Bai Y, Liu Y Q. Spectrum characteristic and information extraction of the iron tailings with high resolution remote sensing[J]. Scientific and Technological Management of Land and Resources, 2015,32(1):79-85.
[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):345-354. doi: 10.3390/e20050345 url: http://www.mdpi.com/1099-4300/20/5/345
[7]	黎静, 伍臣鹏, 刘木华, 等. 高光谱成像的猕猴桃形状特征检测[J]. 光谱学与光谱分析, 2020,40(8):2564-2570.
[7]	Li J, Wu C P, Liu M H, et al. Detection of shape characteristics of kiwifruit based on hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2020,40(8):2564-2570.
[8]	胡敏, 滕文娣, 王晓华, 等. 融合局部纹理和形状特征的人脸表情识别[J]. 电子与信息学报, 2018,40(6):1338-1344.
[8]	Hu M, Teng W T, Wang X H, et al. Facial expression recognition based on local texture and shape features[J]. Journal of Electronics and Information technology, 2012,40(6):1338-1344.
[9]	宋晓宇, 单新建. 用高分辩率卫星影像辨识城市建筑物[J]. 新疆大学学报(自然科学版), 2002(s1):108-111.
[9]	Song X Y, Shan X J. Identification of urban buildings using high resolution satellite images[J]. Journal of Xinjiang University (Natural Science), 2002(s1):108-111.
[10]	黄慧萍. 面向对象影像分析中的尺度问题研究[D]. 北京:中国科学院研究生院(遥感应用研究所), 2003.
[10]	Huang H P. Scale issues in object-oriented image analysis[D]. Beijing:Graduate University of Chinese Academy of Sciences (Institute of Remote Sensing Applications), 2003.
[11]	代晶晶, 吴亚楠, 王登红, 等. 基于面向对象分类的稀土开采区遥感信息提取方法研究[J]. 地球学报, 2018,39(1):111-118.
[11]	Dai J J, Wu Y N, Wang D H, et al. Object-oriented classification for the extraction of remote sensing information in rare earth mining areas[J]. Acta Geoscience Sinica, 2018,39(1):111-118.
[12]	张永彬, 刘佳丽, 汲姣, 等. 露天灰岩矿纹理特征分析及面向对象的分类[J]. 地质与勘探, 2018,54(2):348-357.
[12]	Zhang Y B, Liu J L, Ji J, et al. Texture analysis and object-oriented classification of open limestone mines[J]. Geology and Exploration, 2018,54(2):348-357.
[13]	沈占锋, 骆剑承, 胡晓东, 等. 高分辨率遥感影像多尺度均值漂移分割算法研究[J]. 武汉大学学报(信息科学版), 2010,35(3):313-316.
[13]	Shen Z F, Luo J C, Hu X D, et al. A mean shift multi-scale segmentation for high-resolution remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2010,35(3):313-316.
[14]	王少军, 冯稳, 孟丹, 等. 面向对象分类方法在铁尾矿堆快速提取中的应用研究[J]. 遥感信息, 2012(2):103-107.
[14]	Wang S J, Feng W, Meng D, et al. Rapid information extraction of the iron tailing pile using object-oriented classification technique[J]. Remote Sensing Information, 2012(2):103-107.
[15]	毛宁, 刘慧平, 刘湘平, 等. 基于RMNE方法的多尺度分割最优分割尺度选取[J]. 国土资源遥感, 2019,31(2):10-16.doi: 10.6046/gtzyyg.2019.02.02. doi: 10.6046/gtzyyg.2019.02.02
[15]	Mao N, Liu H P, Liu X P, et al. Optimal scale selection for multi-scale segmentation based on RMNE method[J]. Remote Sensing of Land and Resources, 2019,31(2):10-16.doi: 10.6046/gtzyyg.2019.02.02. doi: 10.6046/gtzyyg.2019.02.02
[16]	陈扬洋, 明冬萍, 徐录, 等. 高空间分辨率遥感影像分割定量实验评价方法综述[J]. 地球信息科学学报, 2017,19(6):818-830. doi: 10.3724/SP.J.1047.2017.00818
[16]	Chen Y Y, Ming D P, Xu L, et al. An overview of quantitative experimental methods for segmentation evaluation of high spatial remote sensing images[J]. Journal of Geo-Information Science, 2017,19(6):818-830.
[17]	孙永军, 童庆禧, 秦其明. 利用面向对象方法提取湿地信息[J]. 国土资源遥感, 2008(1):79-82,108.doi: 10.6046/gtzyyg.2008.01.18. doi: 10.6046/gtzyyg.2008.01.18
[17]	Sun Y J, Tong Q X, Qin Q M. The object-oriented method for wetland information extraction.[J]Remote Sensing of Land and Resources, 2008(1):79-82,108.doi: 10.6046/gtzyyg.2008.01.18. doi: 10.6046/gtzyyg.2008.01.18
[18]	王番, 梁建, 赵海见, 等. 基于形状特征的线状地物提取方法研究[J]. 影像技术, 2014,26(1):50-51,46.
[18]	Wang F, Liang J, Zhao H J, et al. Research on methods of linear feature extraction based on shape feature[J]. Imaging Technology, 2014,26(1):50-51,46.
[19]	李智峰, 朱谷昌, 董泰锋, 等. 基于灰度共生矩阵的图像纹理特征地物分类应用[J]. 地质与勘探, 2011,47(3):456-461.
[19]	Li Z F, Zhu G C, Dong T F, et al. Application of GLCM-Based texture features to remote sensing image classification[J]. Geology and Prospecting, 2011,47(3):456-461.
[20]	Haralick R M. Textural features for image classification[J]. IEEE Transactions on Systems, 1973,3(6):610-621.
[21]	Haralick R M. Statistical and structural approaches to texture[J]. Proceedings of the IEEE, 1973,67(5):786-805. doi: 10.1109/PROC.1979.11328 url: http://ieeexplore.ieee.org/document/1455597/
[22]	Blaschke T, Hay G J, Kelly M, et al. Geographic object-based image analysis:Towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014(87):180-191.
[23]	朱彦光. 基于词包模型的地表矿山要素遥感信息提取方法研究[D]. 长沙:湖南师范大学, 2016.
[23]	Zhu Y G. Remote sensing information extraction for elements of surface mine based on Bag-of-Word model[D]. Changsha:Hunan Normal University, 2016.
[24]	Baatz M, Schape A. Multi-resolution segmentation:An optimization approach for high quality multiscale image segmentation[P].Angewandte Geogr. Information averarbeitung, 2000.
[25]	钟智, 朱曼龙, 张晨, 等. 最近邻分类方法的研究[J]. 计算机科学与探索, 2011,5(5):467-473.
[25]	Zhong Z, Zhu M L, Zhang C, et al. Research on nearest neighbors classification techniques[J]. Journal of Frontiers of Computer Science and Technology, 2011,5(5):467-473.

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