地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (1): 108-116.doi: 10.3724/SP.J.1047.2014.00108

• 遥感科学与应用技术 • 上一篇    下一篇

遥感图像信息容量约束区间的选择与空间分异性

王旭红, 李飞, 张哲, 秦慧杰, 刘晓宁, 李钢   

  1. 西北大学城市与环境学院, 西安 710069
  • 收稿日期:2013-07-16 修回日期:2013-08-21 出版日期:2014-01-05 发布日期:2014-01-05
  • 作者简介:王旭红(1968-),女,陕西咸阳人,副教授,博士,从事遥感图像数据应用与处理、地理信息系统、空间数据挖掘等研究。E-mail:jqy_wxh@163.com
  • 基金资助:

    国家自然科学基金项目(41071271)。

Spatial Variation and Constraint Domain Selection of Remote Sensing Image Information Capacity

WANG Xuhong, LI Fei, ZHANG zhe, QIN Huijie, LIU xiaoning, LI Gang   

  1. College of Urban and Environmental Science, Northwest University, Xi'an 710069, China
  • Received:2013-07-16 Revised:2013-08-21 Online:2014-01-05 Published:2014-01-05

摘要:

遥感图像信息容量是一种能量化表征地表复杂度的评价指标。计算时考虑了像元点所处的整个局部区域特征,其大小与图像灰度层次密切相关,灰度层次越丰富,信息容量的值越大。信息容量模型构建的核心问题是约束区间的选择和参数的确定,合理适宜的参数设置是保证信息容量特性的关键性技术。选取陕西省不同地貌类型区56个样区,以2007年ETM+和2008年SPOT5遥感图像为实验数据。采用了2种不同的约束区间的计算方法,即比较分析和数理统计的方法,分析了遥感图像信息容量约束区间的选择方法和空间分异规律。结果表明,信息容量在一定程度上能有效反映地表空间形态结构的复杂度,信息容量和分形维数、信息熵之间有较好的线性相关性,随着信息容量的增大,样区的分形维数、信息熵也在增大。信息容量的空间分异和陕北黄土高原的黄土地貌形态在空间上的变异是相关的,与陕西关中平原区的地表地物覆盖类型也是相关的,可作为地表形态结构复杂度定量评价指标之一。

关键词: 信息容量, 遥感图像, 空间分异, 约束区间, 地表复杂度

Abstract:

Information capacity is a quantity unit of pixel density information. Center pixel and neighboring pixels will all be taken into account in the calculation of information capacity. The value of information capacity is closely related to the image gray levels. The more the gray level is, the greater the information capacity value will be. Thus, information capacity can objectively and effectively express land surface spatial structural information. However, the core issue of information capacity theory is the selection of the constraint domain and the determination of parameters. And appropriate setting of parameters is a key technology to ensure the accurateness of information capacity. In this study, 56 different landform areas of Shaanxi Province were selected as test areas, using the research result of remote sensing images in 2007 ETM + and 2008 SPOT5 as experimental data. According to this, two different calculation method of constraints domain in information capacity were adopted by using comparative analysis and mathematical statistics, which analyzed constraint domain selection and spatial distribution of the remote sensing image information capacity. All these experimental results show that information capacity can reflect the surface spatial structure complexity to a certain extent, and it exits a better linear relationship between information capacity and fractal dimension / information entropy, respectively. Information capacity also increases with the increase of fractal dimension and information entropy. Spatial distribution of information capacity is correlative with topographic feature of loess landform, as the same correlation with the surface spatial structure complexity of land cover types in the Central Shaanxi Plain. So, information capacity can be taken as a discriminate factor to identify the surface complexity.

Key words: remote sensing image, land surface complexity, constraint domain, spatial variation, information capacity