基于优化地貌特征和纹理信息的黄土高原沟缘线提取方法
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摘要
沟缘线是黄土高原地区的典型特征线,其提取结果是构建地表空间分布式水土流失过程模型的基础。本文提出了基于优化地貌特征和纹理信息的面向对象沟缘线自动提取方法,即通过构建适宜的地形因子和纹理信息,利用地物的几何结构、地形纹理和相邻像元差异性的关系,采用面向对象多尺度分割技术,基于决策树分类的方法提取沟缘线。选取沟缘线发育典型的黄土高原地区,以1∶10 000的DEM为数据源,确定了最优的分割参数和分类特征。相对于人工识别的沟缘线,该方法提取的结果在4个像元缓冲范围内为90%,绝对误差均值为2～3个像元,最大误差为4～6个像元。通过与基于坡面形态特征及汇水过程特点的提取算法进行对比,可知该方法克服了传统从形态特征出发的自动提取结果出现大量噪声的缺陷,提高了沟缘线提取精度,位置准确性增强,为黄土高原大范围宏观地貌分异研究提供了有效的方法和途径。
The extraction of shoulder lines of valleys on the Loess Plateau is the basis of constructing the surficial spatial distribution of soil and water loss. A shoulder-line extraction method was proposed by using optimized topographical texture information. Based on the 1 ∶ 10 000 DEM data and GF-1 remote sensing images,areas with obvious shoulder lines of valleys on the Loess Plateau were taken as the research object. Considering texture features of the gray level co-occurrence matrix of the terrain,along with geometrical structures of ground objects,terrain texture and differences of adjacent pixels,the gully edge of the typical watershed was extracted by using the object-oriented method. The extraction results were further validated by comparing with the morphological characteristics of the slope and the draining characteristics of the catchment. The results showed that elevation,illumination simulation,surface depth of cut,slope and other related weakness,as well as the topography texture features such as homogeneity,variance,contrast, correlation, etc., were employed in the ridge-line extraction. Compared with empirical interpretation based on experts' knowledge,offsets within four pixel for over 90% of the grid,with the average of absolute errors within 2 ～ 3 pixel and the maximum error was 4 ～ 6 pixel. The method fully exploited the features of strong correlation and heterogeneity between pixels in the upstream and downstream of the ditch margin,which had strong anti-noise ability and effectively weakened the mixing of positive and negative topography units. It also reduced the data redundancy of post-processing,and realized the balance of classification accuracy,efficiency and operability,which provided an effective method and approach for the macroscopic landform differentiation on the Loess Plateau.
The extraction of shoulder lines of valleys on the Loess Plateau is the basis of constructing the surficial spatial distribution of soil and water loss. A shoulder-line extraction method was proposed by using optimized topographical texture information. Based on the 1 ∶ 10 000 DEM data and GF-1 remote sensing images,areas with obvious shoulder lines of valleys on the Loess Plateau were taken as the research object. Considering texture features of the gray level co-occurrence matrix of the terrain,along with geometrical structures of ground objects,terrain texture and differences of adjacent pixels,the gully edge of the typical watershed was extracted by using the object-oriented method. The extraction results were further validated by comparing with the morphological characteristics of the slope and the draining characteristics of the catchment. The results showed that elevation,illumination simulation,surface depth of cut,slope and other related weakness,as well as the topography texture features such as homogeneity,variance,contrast, correlation, etc., were employed in the ridge-line extraction. Compared with empirical interpretation based on experts' knowledge,offsets within four pixel for over 90% of the grid,with the average of absolute errors within 2 ～ 3 pixel and the maximum error was 4 ～ 6 pixel. The method fully exploited the features of strong correlation and heterogeneity between pixels in the upstream and downstream of the ditch margin,which had strong anti-noise ability and effectively weakened the mixing of positive and negative topography units. It also reduced the data redundancy of post-processing,and realized the balance of classification accuracy,efficiency and operability,which provided an effective method and approach for the macroscopic landform differentiation on the Loess Plateau.
引文
[1]景可.黄土高原沟谷侵蚀研究[J].地理科学,1986,6(4):340-347.JING Ke. A study on gully erosion on the Loess Plateau[J]. Scientia Geographica Sinica,1986,6(4):340-347.(in Chinese)
[2]闾国年,钱亚东,陈钟明.基于栅格数字高程模型提取特征地貌技术研究[J].地理学报,1998,53(6):562-570.LGuonian,QIAN Yadong,CHEN Zhongming. Automated extraction of the characteristics of topography from grid digital elevation data[J]. Acta Geographica Sinica,1998,53(6):562-570.(in Chinese)
[3]闾国年,钱亚东,陈钟明.基于栅格数字高程模型自动提取黄土地貌沟沿线技术研究[J].地理科学,1998,18(6):567-573.LGuonian,QIAN Yadong,CHEN Zhongming. Study of automated extraction of shoulder line of valley from grid digital elevation data[J]. Scientia Geographica Sinica,1998,18(6):567-573.(in Chinese)
[4] VOGT J V,COLOMBO R,BERTOLO F. Deriving drainage networks and catchment boundaries:a new methodology combining digital elevation data and environmental characteristics[J]. Geomorphology,2003,53(3):281-298.
[5] CHONG L,YANG D W. Deriving drainage networks and catchment boundaries from grid digital elevation model[J]. Journal of China Institute of Water,2004(3):50-56.
[6] SHENG J,TANG G,KAI L. A new extraction method of loess shoulder-line based on marr-hildreth operator and terrain mask[J]. Plos One,2014,10(4):e0123804.
[7]刘鹏举,朱清科,吴东亮,等.基于栅格DEM与水流路径的黄土区沟缘线自动提取技术研究[J].北京林业大学学报,2006,28(4):72-76.LIU Pengju,ZHU Qingke,WU Dongliang,et al. Automated extraction of shoulder line of valleys based on flow paths from grid digital elevation model(DEM)data[J]. Journal of Beijing Forestry University,2006,28(4):72-76.(in Chinese)
[8]朱红春,汤国安,张友顺,等.基于DEM提取黄土丘陵区沟沿线[J].水土保持通报,2003,23(5):43-46.ZHU Hongchun,TANG Guoan,ZHANG Youshun,et al. Thalweg in loess hill area based on DEM[J]. Bulletin of Soil&Water Conservation,2003,23(5):43-46.(in Chinese)
[9]周毅,汤国安,王春,等.基于高分辨率DEM的黄土地貌正负地形自动分割技术研究[J].地理科学,2010,30(2):261-266.ZHOU Yi,TANG Guoan,WANG Chun,et al. Automatic segmentation of loess positive and negative terrains based on high resolution grid DEMs[J]. Scientia Geographica Sinica,2010,30(2):261-266.(in Chinese)
[10]李敏,杨昕,陈盼盼,等.面向点云数据的黄土丘陵沟壑区沟沿线自动提取方法[J].地球信息科学学报,2016,18(7):869-877.LI Min,YANG Xin,CHEN Panpan,et al. Method of automatic shoulder line extraction in the loess hilly area based on point cloud data[J]. Journal of Geo-Information Science,2016,18(7):869-877.(in Chinese)
[11]王轲,王琤,张青峰,等.地形开度和差值图像阈值分割原理相结合的黄土高原沟沿线提取法[J].测绘学报,2015,44(1):67-75.WANG Ke,WANG Zheng,ZHANG Qingfeng,et al. Loess shoulder line extraction based on openness and threshold segmentation[J]. Acta Geodaetica et Cartographica Sinica,2015,44(1):67-75.(in Chinese)
[12]晏实江,汤国安,李发源,等.利用DEM边缘检测进行黄土地貌沟沿线自动提取[J].武汉大学学报(信息科学版),2011,36(3):363-367.YAN Shijiang,TANG Guoan,LI Fayuan,et al. An edge detection based method for extraction of loess shoulder-line from grid DEM[J]. Geomatics&Information Science of Wuhan University,2011,36(3):363-367.(in Chinese)
[13]齐威.黄土陷穴的自动提取与分析研究[D].南京:南京信息工程大学,2017.QI Wei. Research on automatic extraction and analysis of loess sinking hole[D]. Nanjing:Nanjing University of Information Science&Techology,2017.(in Chinese)
[14]田丹,刘爱利,丁浒,等.地貌形态类型面向对象分类法的改进[J].地理与地理信息科学,2016,32(2):46-52.TIAN Dan,LIU Aili,DING Hu,et al. Improvement of object-oriented classification method for landform types[J]. Geography and Geo-Information Science,2016,32(2):46-52.(in Chinese)
[15] SHRUTHI R B V,KERLE N,JETTEN V,et al. Quantifying temporal changes in gully erosion areas with object oriented analysis[J]. Catena,2015,128:262-277.
[16]王芸,刘文兆,李怀有,等.黄土高塬沟壑区砚瓦川流域水沙关系及其对流域治理的响应[J].自然资源学报,2015,30(8):1403-1413.WANG Yun,LIU Wenzhao,LI Huaiyou,et al. The flow-sediment relationship and its response to watershed management in Yanwachuan Watershed,Loess Plateau Gully Region,China[J]. Journal of Natural Resources,2015,30(8):1403-1413.(in Chinese)
[17] ILEA D E,WHELAN P F. Image segmentation based on the integration of colour-texture descriptors—a review[J]. Pattern Recognition,2011,44(10):2479-2501.
[18] DUTTA S,DATTA A,CHAKLADAR N D,et al. Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique[J]. Precision Engineering,2012,36(3):458-466.
[19] BLASCHKE T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16.
[20] LUCIAN D,CLEMENS E. Automated object-based classification of topography from SRTM data[J]. Geomorphology,2012,141-142(4):21-33.
[21] YONG Liu. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,68(1):144-156.
[22] QI Y,HUANG F,QI X. Feature extraction and scale analysis based on Quickbird image using object-oriented approach[J].Proceedings of SPIE-The International Society for Optical Engineering,2008,7147:27-36.
[23] VICTOR A A Q. Metaheuristics for supervised parameter tuning of multiresolution segmentation[J]. IEEE Geoscience and Remote Sensing Letters,2016,13(9):1364-1368.
[2]闾国年,钱亚东,陈钟明.基于栅格数字高程模型提取特征地貌技术研究[J].地理学报,1998,53(6):562-570.LGuonian,QIAN Yadong,CHEN Zhongming. Automated extraction of the characteristics of topography from grid digital elevation data[J]. Acta Geographica Sinica,1998,53(6):562-570.(in Chinese)
[3]闾国年,钱亚东,陈钟明.基于栅格数字高程模型自动提取黄土地貌沟沿线技术研究[J].地理科学,1998,18(6):567-573.LGuonian,QIAN Yadong,CHEN Zhongming. Study of automated extraction of shoulder line of valley from grid digital elevation data[J]. Scientia Geographica Sinica,1998,18(6):567-573.(in Chinese)
[4] VOGT J V,COLOMBO R,BERTOLO F. Deriving drainage networks and catchment boundaries:a new methodology combining digital elevation data and environmental characteristics[J]. Geomorphology,2003,53(3):281-298.
[5] CHONG L,YANG D W. Deriving drainage networks and catchment boundaries from grid digital elevation model[J]. Journal of China Institute of Water,2004(3):50-56.
[6] SHENG J,TANG G,KAI L. A new extraction method of loess shoulder-line based on marr-hildreth operator and terrain mask[J]. Plos One,2014,10(4):e0123804.
[7]刘鹏举,朱清科,吴东亮,等.基于栅格DEM与水流路径的黄土区沟缘线自动提取技术研究[J].北京林业大学学报,2006,28(4):72-76.LIU Pengju,ZHU Qingke,WU Dongliang,et al. Automated extraction of shoulder line of valleys based on flow paths from grid digital elevation model(DEM)data[J]. Journal of Beijing Forestry University,2006,28(4):72-76.(in Chinese)
[8]朱红春,汤国安,张友顺,等.基于DEM提取黄土丘陵区沟沿线[J].水土保持通报,2003,23(5):43-46.ZHU Hongchun,TANG Guoan,ZHANG Youshun,et al. Thalweg in loess hill area based on DEM[J]. Bulletin of Soil&Water Conservation,2003,23(5):43-46.(in Chinese)
[9]周毅,汤国安,王春,等.基于高分辨率DEM的黄土地貌正负地形自动分割技术研究[J].地理科学,2010,30(2):261-266.ZHOU Yi,TANG Guoan,WANG Chun,et al. Automatic segmentation of loess positive and negative terrains based on high resolution grid DEMs[J]. Scientia Geographica Sinica,2010,30(2):261-266.(in Chinese)
[10]李敏,杨昕,陈盼盼,等.面向点云数据的黄土丘陵沟壑区沟沿线自动提取方法[J].地球信息科学学报,2016,18(7):869-877.LI Min,YANG Xin,CHEN Panpan,et al. Method of automatic shoulder line extraction in the loess hilly area based on point cloud data[J]. Journal of Geo-Information Science,2016,18(7):869-877.(in Chinese)
[11]王轲,王琤,张青峰,等.地形开度和差值图像阈值分割原理相结合的黄土高原沟沿线提取法[J].测绘学报,2015,44(1):67-75.WANG Ke,WANG Zheng,ZHANG Qingfeng,et al. Loess shoulder line extraction based on openness and threshold segmentation[J]. Acta Geodaetica et Cartographica Sinica,2015,44(1):67-75.(in Chinese)
[12]晏实江,汤国安,李发源,等.利用DEM边缘检测进行黄土地貌沟沿线自动提取[J].武汉大学学报(信息科学版),2011,36(3):363-367.YAN Shijiang,TANG Guoan,LI Fayuan,et al. An edge detection based method for extraction of loess shoulder-line from grid DEM[J]. Geomatics&Information Science of Wuhan University,2011,36(3):363-367.(in Chinese)
[13]齐威.黄土陷穴的自动提取与分析研究[D].南京:南京信息工程大学,2017.QI Wei. Research on automatic extraction and analysis of loess sinking hole[D]. Nanjing:Nanjing University of Information Science&Techology,2017.(in Chinese)
[14]田丹,刘爱利,丁浒,等.地貌形态类型面向对象分类法的改进[J].地理与地理信息科学,2016,32(2):46-52.TIAN Dan,LIU Aili,DING Hu,et al. Improvement of object-oriented classification method for landform types[J]. Geography and Geo-Information Science,2016,32(2):46-52.(in Chinese)
[15] SHRUTHI R B V,KERLE N,JETTEN V,et al. Quantifying temporal changes in gully erosion areas with object oriented analysis[J]. Catena,2015,128:262-277.
[16]王芸,刘文兆,李怀有,等.黄土高塬沟壑区砚瓦川流域水沙关系及其对流域治理的响应[J].自然资源学报,2015,30(8):1403-1413.WANG Yun,LIU Wenzhao,LI Huaiyou,et al. The flow-sediment relationship and its response to watershed management in Yanwachuan Watershed,Loess Plateau Gully Region,China[J]. Journal of Natural Resources,2015,30(8):1403-1413.(in Chinese)
[17] ILEA D E,WHELAN P F. Image segmentation based on the integration of colour-texture descriptors—a review[J]. Pattern Recognition,2011,44(10):2479-2501.
[18] DUTTA S,DATTA A,CHAKLADAR N D,et al. Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique[J]. Precision Engineering,2012,36(3):458-466.
[19] BLASCHKE T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16.
[20] LUCIAN D,CLEMENS E. Automated object-based classification of topography from SRTM data[J]. Geomorphology,2012,141-142(4):21-33.
[21] YONG Liu. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,68(1):144-156.
[22] QI Y,HUANG F,QI X. Feature extraction and scale analysis based on Quickbird image using object-oriented approach[J].Proceedings of SPIE-The International Society for Optical Engineering,2008,7147:27-36.
[23] VICTOR A A Q. Metaheuristics for supervised parameter tuning of multiresolution segmentation[J]. IEEE Geoscience and Remote Sensing Letters,2016,13(9):1364-1368.