基于面向对象的铁尾矿信息提取技术研究——以迁西地区北京二号遥感影像为例

doi:10.6046/zrzyyg.2021027

摘要
图/表
参考文献
相关文章
Metrics

全文: PDF(7269 KB)

HTML
输出: BibTeX | EndNote (RIS)

摘要

实现目标区域尾矿信息的识别和提取是矿山环境动态监测的重要组成部分。中低空间分辨率影像多是基于光谱信息进行地物分类,但由于矿区环境特殊,部分道路与尾矿的光谱反射率相近,仅利用光谱信息进行地物分类易将尾矿错误划分为道路,影响尾矿库结构完整性以及其占地面积统计。针对这一问题,基于北京二号高空间分辨率影像对迁西地区铁尾矿的光谱特征、形状特征以及纹理特征进行综合分析,提出了一种基于多特征的面向对象分类方法。首先,对北京二号影像进行多尺度分割,并以地物在各波段的反射率及光谱差值作为地物光谱特征值; 然后,利用协方差矩阵和对象边界提取长宽比作为地物形状特征值; 再利用主成分波段进行灰度共生矩阵计算,并从中选取对比度、相关度、熵这3个能有效区分尾矿与其他地物纹理特点的值作为遥感图像的纹理特征值; 最后,结合以上地物特征信息利用最近邻方法实现面向对象分类并进行精度评价。结果表明: 该方法可有效避免尾矿库内道路的误分,为开展大范围高精度尾矿识别与动态监测提供研究基础。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	范莹琳
	娄德波
	张长青
	魏英娟
	贾福东

关键词 ：铁尾矿, 特征选取, 影像分割, 面向对象分类

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.

Key words： iron tailings feature selection image segmentation object-oriented classification

收稿日期: 2021-01-22 出版日期: 2021-12-23

ZTFLH:

TP79

基金资助:中国地质调查项目“津冀重要矿产资源集中区资源综合利用与评价”(DD20190182)

作者简介: 范莹琳(1996-),女,硕士研究生,地质工程专业(遥感地质方向)。Email: 18811458838@163.com。

引用本文:

范莹琳, 娄德波, 张长青, 魏英娟, 贾福东. 基于面向对象的铁尾矿信息提取技术研究——以迁西地区北京二号遥感影像为例[J]. 自然资源遥感, 2021, 33(4): 153-161.
FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. 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. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021027 或 https://www.gtzyyg.com/CN/Y2021/V33/I4/153

Fig.1 研究区地理位置

Fig.2 研究区全色波段与多光谱波段融合后的图像

Tab.1 北京二号图像波段信息

Fig.3 研究区地物光谱曲线

Fig.4 地物长宽比特征

Fig.5 地物纹理特征提取结果

Fig.6 决定多尺度分割效果的异质性因子

Fig.7 紧致度因子为0.5分割结果(分割尺度,形状因子,紧致度因子)

Fig.8 形状因子为0.4分割结果(分割尺度,形状因子,紧致度因子)

Fig.9 ROC-LV曲线图

Fig.10 分割尺度为100,形状因子为0.4,紧致度因子为0.5

Fig.11 面向对象分类与基于像元分类结果图

Fig.12 面向对象与基于像元方法铁尾矿提取效果图

Tab.2 面向对象分类与基于像元分类精度评价

[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
[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.
	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
[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
	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.
	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
[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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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
	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
	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
	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.
	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.
	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
[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.
	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.
	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.

[1]	石敏, 李慧颖, 贾明明. 基于GEE云平台与Landsat数据的山口自然保护区红树林时空变化分析[J]. 自然资源遥感, 2023, 35(2): 61-69.
[2]	李天驰, 王道儒, 赵亮, 凡仁福. 基于Landsat8遥感数据的西沙群岛永乐环礁底质分类与变化分析[J]. 自然资源遥感, 2023, 35(2): 70-79.
[3]	夏既胜, 马梦莹, 符钟壬. 基于GF-2遥感影像的机械性破损面提取方法[J]. 国土资源遥感, 2020, 32(2): 26-32.
[4]	朱士才, 翟晓彤, 王宗伟. 基于Mean Shift的大批量遥感影像分割方法[J]. 国土资源遥感, 2020, 32(1): 13-18.
[5]	娄佩卿, 陈晓雨, 王疏桐, 付波霖, 黄永怡, 唐廷元, 凌铭. 基于无人机影像的喀斯特农耕区地物识别——以桂林市为例[J]. 国土资源遥感, 2020, 32(1): 216-223.
[6]	杨丽萍, 马孟, 谢巍, 潘雪萍. 干旱区Landsat8全色与多光谱数据融合算法评价[J]. 国土资源遥感, 2019, 31(4): 11-19.
[7]	翟德超, 范亚男, 周亚男. 融入边界特征的遥感影像多尺度分割[J]. 国土资源遥感, 2019, 31(3): 36-42.
[8]	姚丙秀, 黄亮, 许艳松. 一种结合超像素和图论的高空间分辨率遥感影像分割方法[J]. 国土资源遥感, 2019, 31(3): 72-79.
[9]	黄惠, 郑雄伟, 孙根云, 郝艳玲, 张爱竹, 容俊, 马红章. 基于引力自组织神经网络的震害遥感影像分类[J]. 国土资源遥感, 2019, 31(3): 95-103.
[10]	陈震, 张耘实, 章远钰, 桑玲玲. 高标准农田建后遥感监测方法[J]. 国土资源遥感, 2019, 31(2): 125-130.
[11]	杨军, 裴剑杰. 一种改进的ICM遥感影像分割算法[J]. 国土资源遥感, 2018, 30(3): 18-25.
[12]	李亮, 王蕾, 王凯, 李胜. 基于像斑异质度的矢量图与遥感影像变化检测[J]. 国土资源遥感, 2018, 30(1): 30-36.
[13]	朱红春, 黄伟, 刘海英, 张忠芳, 王彬. 基于KL散度的面向对象遥感变化检测[J]. 国土资源遥感, 2017, 29(2): 46-52.
[14]	苏腾飞, 张圣微, 李洪玉. 基于纹理特征与区域生长的高分辨率遥感影像分割算法[J]. 国土资源遥感, 2017, 29(2): 72-81.
[15]	滑永春, 李增元, 高志海, 郭中. 基于GF-2民勤县白刺包提取技术[J]. 国土资源遥感, 2017, 29(1): 71-77.

Viewed

Full text

Abstract

Cited

Shared

Discussed