植物生态学报 ›› 2005, Vol. 29 ›› Issue (6): 927-933.DOI: 10.17521/cjpe.2005.0121

• 论文 • 上一篇    下一篇

基于地形限制特征的泾河流域遥感地表覆被分类

洪军1,2, 葛剑平2,*(), 蔡体久2,3, 聂忆黄4   

  1. 1 农业部全国畜牧兽医总站,北京 100026
    2 北京师范大学生态研究所,生物多样性与生态工程重点实验室,北京 100875
    3 东北林业大学林学院,哈尔滨 150040
    4 中国环境科学研究院,北京 100012
  • 收稿日期:2004-10-13 接受日期:2005-03-29 出版日期:2004-10-13 发布日期:2005-09-30
  • 通讯作者: 葛剑平
  • 作者简介:*E-mail:gejp@bnu.edu.cn
  • 基金资助:
    科技部973项目(2002CB111507)

LAND COVER CLASSIFICATION OF REMOTELY SENSED IMAGERY USING A METHOD BASED ON TOPOGRAPHICAL RESTRICTIVE FEATURES: A CASE STUDY OF THE JINHE WATERSHED

HONG Jun1,2, GE Jian-Ping2,*(), CAI Ti-Jiu2,3, NIE Yi-Huang4   

  1. 1 China Animal Husbandry & Veterinary Headstation, Beijing 100026, China
    2 Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering & Institute of Ecology, Beijing Normal University, Beijing 100875, China
    3 College of Forestry, North-East Forest University, Harbin 150040, China
    4 Chinese Research Academy of Environment Science, Beijing 100012, China
  • Received:2004-10-13 Accepted:2005-03-29 Online:2004-10-13 Published:2005-09-30
  • Contact: GE Jian-Ping

摘要:

由于在分类方法和空间分辨率等方面存在局限性,基于粗分辨率遥感数据的传统非监督分类结果在不同地物过渡带内往往误差较大。该文提出了基于地形限制特征的分类方法,在非监督分类的基础上,将非监督分类结果按照像元进行细分,并运用地形限制条件对细分后的像元进行二次判别分类。结果表明,分类精度明显提高,其中,农田和居民点分类精度的提高最为明显。这一方法使得完全同质的单元可以进行属性的变更,改善了像元空间分辨率差造成的误差;而地形限制特征的引入减少了传统非监督分类的不确定性,使模糊区域的分类有了较为明确的区分特征,提高了分类的精度。

关键词: 像元细分, 地形限制特征, 遥感植被分类, 泾河流域

Abstract:

Based on non-supervised classification methods, multi-temporal images, such as NOAA-AVHRR, SPOT-VEGETATION and MODIS series data, have been used to map regional, continental or global land cover patterns. Because of limitations of the classification method and spatial resolution of images, traditional non-supervised classification often results in many errors in the transition zones in which the spatial distribution of vegetation show segmental patterns. This problem can decrease the integral accuracy of the classification to a certain extent.
In this paper, a new method, the topographical restrictive features, is presented to classify remotely sensed images using the Jinhe watershed in the Loess plateau as a case study. First, 25 relatively independent classes were obtained using the non-supervised classification method with multi-temporal NDVI data derived from the first two bands of MODIS data. Every cell with 500 m spatial resolution of the classification determined by the non-supervised method was divided into 25 homogenous cells with 100 m spatial resolution. Then, topographical features were defined. Information about aspect, slope, elevation, river net structure, and vegetation regional characteristics, derived from 1:250 000 geographical spatial data, ETM+ image and yield data, were used to construct topographical restrictive features. Finally, the classification of every sub-cell was tested using the restrictive features and some cells were reclassified while maintaining their original classification. After the secondary classification, all cells were labeled by land cover type according to the land cover classification system, which was defined previously on the base of the IGBP land cover classification system and UMD system.
Using this new method, the accuracy of the land cover classification increased from 71.88%, in traditional non-supervised classification method, to 84.09%. In particular, the accuracy of cropland and urban type classification improved. The method of cell sub-division avoids the shortcomings of traditional classification methods owing to the coarse resolution of the image processing, and makes it more highly probable that land cover types are homogenous within cells. The introduction of topographical restrictive features decreases uncertainty of traditional fuzzy classification and provides more precise distinctive features to classify the fuzzy zone, and thus improves the accuracy of classification.

Key words: Image segmentation, Topographical restrictive features, Remotely sensed land cover classification, Jinhe watershed