地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (6): 751-760.doi: 10.3724/SP.J.1047.2012.00751

• 本期要文(可全文下载) • 上一篇    下一篇

基于灰度共生矩阵的DEM地形纹理特征量化研究

刘凯1, 汤国安1, 陶旸1,2, 蒋圣1,2   

  1. 1. 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023;
    2. 江苏省基础地理信息中心, 南京 210013
  • 收稿日期:2012-09-06 修回日期:2012-11-01 出版日期:2012-12-25 发布日期:2012-12-25
  • 通讯作者: 汤国安(1961-),男,浙江宁波人,博士生导师,教授,主要研究方向为地理信息系统、DEM与数字地形分析及GIS空间分析。E-mail:tangguoan@njnu.edu.cn E-mail:tangguoan@njnu.edu.cn
  • 作者简介:刘凯(1989-),男,江苏镇江人,硕士研究生,主要从事DEM与数字地形分析研究。E-mail:lklkymym@163.com
  • 基金资助:

    国家自然科学基金项目(40930531、41171320、41201415);江苏省自然科学基金项目(BK2012504)。

GLCM Based Quantitative Analysis of Terrain Texture from DEMs

LIU Kai1, TANG Guo'an1, TAO Yang1,2, JIANG Sheng1,2   

  1. 1. Key Laboratory of Virtual Geographical Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
    2. Geomatics Center of Jiangsu Province, Nanjing 210013, China
  • Received:2012-09-06 Revised:2012-11-01 Online:2012-12-25 Published:2012-12-25

摘要:

DEM的地形纹理以其表达地形表面的纯粹性与分析数据的可派生性受到越来越多关注。本文选取陕西省10个不同地貌类型区的25m分辨率DEM数据,引入空间灰度共生矩阵(GLCM)对地形表面纹理特征进行定量分析。研究表明,25m分辨率DEM数据的GLCM模型适宜分析间距是大于等于3个栅格大小。各纹理参数中,相关度可用于地形纹理的方向性量化;方差、差的方差、对比度可用于对地形纹理的周期性分析;熵、二阶角矩、逆差矩可用于对地形纹理的复杂性分析。在DEM及其派生数据中,光照模拟数据计算的各纹理参数的平均变异系数最高,表明光照模拟数据最适合于地形纹理特征的量化研究。同时本文提出了一种多参数综合的地形纹理量化方法,通过运用综合周期性和综合复杂性两个指标对不同地形区量化分析,结果表明,这两个指标对不同地形形态响应显著,可用于地形形态分类与识别研究。

关键词: 地形纹理, DEM, 量化分析, 灰度共生矩阵

Abstract:

Terrain texture is an important natural texture. DEM based terrain texture attracts more attention in the research area for its purity in representing surface topography and its derivability in terrain analysis. In this paper, 10 sample areas from different landform types of Shaanxi Province were selected to make a quantitative analysis on the terrain texture by Gray level co-occurrence matrix (GLCM) model. Experiments show that, when using the DEM data with 25m resolution, the suitable analytic distance of GLCM model is not less than 3 pixels. Among all the parameters in the model, correlation could be used for texture direction detection. Contrast, variance, and different variance could be applied for texture periodicity analysis. Entropy, angular second moment and inverse different moment are suitable for texture complexity investigation. In this research, quantitative analysis is conducted to terrain texture by using DEM data, hillshade data, slope data and curvature data. The terrain texture directivity experiment shows that the correlation of hillshade data reacts sensitively to the terrain texture direction and can detect main terrain texture direction. The correlation of slope data reacts obviously in rugged topography such as hilly region and mountainous regions so it can play an auxiliary role for hillshade data in the detecting of terrain texture direction. Results of terrain texture periodicity and complexity analysis shows that among DEM data and its derived data, the mean variation coefficient of each texture parameter based on hillshade data is the highest, and it further proves that the hillshade data is most suitable for quantitative analysis of terrain texture. Quantification is conducted by variance of hillshade data to texture periodicity of different terrain texture, variance eigenvalue of flat, platform, hill and mountain region gradually increases which indicates the increase of terrain texture periodicity. Analysis is also conducted to the terrain texture complexity through angular second moment parameters computed by hillshade data. Eigenvalue has clear peak value in the sample region of flat and the eigenvalue of platform decreases obviously. Eigenvalue of hills and mountain region verge to zero which shows that texture of plat has lowest complexity, followed by the lower complexity of platform and the highest complexity of hills and mountain region. This paper also proposed a multi-parameter integrated method which employs both comprehensive periodicity and comprehensive complexity in terrain texture quantitative analysis. This method not only reduces replicate analyses but also makes full use of various texture parameter information, it also unifies range through normalization for the convenience of quantitative analysis. The result showed that these two parameters have significant response to the different terrain texture, which shows a great potential in landform recognition and classification.

Key words: quantization analysis, gray level co-occurrence matrix, DEM, terrain texture