地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (4): 540-548.doi: 10.3724/SP.J.1047.2017.00540

• 遥感科学与应用技术 • 上一篇    下一篇

遥感影像空间格局变异函数分析研究进展

卫春阳1,2(), 徐丹丹1,2, 董凯凯1,2, 刘兆礼1,*()   

  1. 1. 中国科学院东北地理与农业生态研究所,长春 130102
    2. 中国科学院大学,北京 100049
  • 收稿日期:2016-10-08 修回日期:2017-01-09 出版日期:2017-04-20 发布日期:2017-04-20
  • 通讯作者: 刘兆礼 E-mail:990314078@qq.com;liuzhaoli@iga.ac.cn
  • 作者简介:

    作者简介:卫春阳(1991-),男,四川雅安人,硕士生,主要从事遥感影像空间格局研究。E-mail:990314078@qq.com

  • 基金资助:
    国家重点研发计划项目(2016YFC0500204)

Advances in Analysis of Remote Sensing Image Pattern Based on Semi-variogram

WEI Chunyang1,2(), XU Dandan1,2, DONG Kaikai1,2, LIU Zhaoli1,*()   

  1. 1. Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2016-10-08 Revised:2017-01-09 Online:2017-04-20 Published:2017-04-20
  • Contact: LIU Zhaoli E-mail:990314078@qq.com;liuzhaoli@iga.ac.cn

摘要:

随着多光谱传感器的广泛运用,利用地物光谱响应特征提取地表信息的技术日益成熟,但是由于地表状况的复杂性和光谱响应的局限性,光谱方法在指示平均大小、空间异向性、空间分布、空间异质性等格局信息方面存在不足,因此挖掘遥感影像的空间格局特征日益受到研究者的重视。已有研究发现,变异参数与地表场景参数存在一定的对应关系,通过变异参数可以实现地表场景参数的提取,因此变异函数分析方法被广泛应用于真实遥感影像格局分析中,具体包括平均尺度提取、周期性格局探测、空间异质性表征与空间异向性描述等地表格局参数量化方面、最佳尺度选择与影像纹理分析等遥感影像信息提取方面。尽管变异函数分析方法在上述应用领域中都发挥了重要作用,但是当前利用变异函数进行遥感影像空间格局分析大多局限于定性描述层面,缺乏精确化的量化描述与分析,限制了变异函数分析方法应用的进一步拓展,究其原因在于对遥感影像格局变异函数分析的内在机制缺乏深入了解。本文回顾了近20年来变异函数分析方法在遥感格局分析领域的主要应用,并对该方法本身的优势和存在的不足进行了总结,可为变异函数这一工具在遥感影像格局分析方面的有效应用提供参考。

关键词: 变异函数, 遥感影像, 地表格局特征, 空间尺度, 纹理分类

Abstract:

With the widespread application of multispectral sensors, the spectral response characteristics of features have been increasingly used to extract the surface information. Due to the complexity of surface conditions and limitation of spectral responses, spectral methods of indicating the average size, spatial distribution, spatial heterogeneity and pattern information are insufficient. Therefore, considerable attentions have been paid by researchers to mine the spatial pattern of remote sensing images. Previous studies have found that there is a corresponding relationship between semi-variogram parameters and scene parameters. Researchers can extract the surface parameters using semi-variogram parameters. With the understanding of this relationship, variogram analysis methods are widely used to quantify the surface pattern parameters, including the average scale extraction, periodic pattern detection, spatial heterogeneity and spatial anisotropy, extraction of image information of best scale selection, and texture analysis in pattern analysis of remote sensing images. Although the analysis methods of variogram play an important role in the above-mentioned application fields, the analysis of spatial pattern of remote sensing images based on the variogram is mostly limited to the qualitative description levels, and lacks precise quantitative description and analysis, which restricts the further application of the variogram analysis method. The main problem is the lack of understanding about the inherent mechanisms of the analysis of pattern variogram of remote sensing images. This should be the future direction in the research. This paper reviews the application of variogram analysis method in the field of remote sensing pattern analysis over the past two decades. The advantages and disadvantages of the method are summarized, which can provide a reference for the effective application of the variogram in the analysis of remote sensing image pattern.

Key words: variogram, remote sensing images, surface pattern characteristics, spatial scale, texture classification