地理科学 ›› 2013, Vol. 33 ›› Issue (11): 1395-1399.doi: 10.13249/j.cnki.sgs.2013.011.1395

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基于地物光谱特征和空间特征的干旱区绿洲土地分类

裴欢1(), 房世峰2   

  1. 1. 燕山大学信息科学与工程学院, 河北 秦皇岛 066004
    2.中国科学院地理科学与资源研究所, 北京 100101
  • 收稿日期:2012-11-26 修回日期:2013-01-04 出版日期:2013-11-07 发布日期:2013-11-07
  • 作者简介:

    作者简介:裴 欢(1982-),女,甘肃民勤县人,博士,讲师,研究方向为资源环境遥感。E-mail:1982197950@163.com

  • 基金资助:
    国家自然科学基金(41201097)、绿洲生态教育部重点实验室开放基金(XJDX0206-2011-01)、冰冻圈科学国家重点实验室开放基金(SKLCS 2011-08)项目资助

Land Classification of Arid Oasis Based on Spectral and Spatial Feature of Ground Objects

Huan PEI1(), Shi-feng FANG2   

  1. 1. College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
    2.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2012-11-26 Revised:2013-01-04 Online:2013-11-07 Published:2013-11-07

摘要:

对SPOT数据主成分变换、缨帽变换、最小噪声分离等变换,计算植被指数和湿度指数,构建12个光谱特征,结合DEM及提取的坡度空间特征,形成14个分类特征。采用欧氏距离法,对不同波段组合下地物类间分离性进行了统计,进行特征选择。在此基础上,根据混淆地物在特征波段的分布特点,建立决策树分类模型,提取研究区土地利用/覆盖信息。研究表明,利用多地表特征参数的决策树分类方法与传统分类方法相比,分类精度有明显的提高。为绿洲土地分类提供了一定的参考。

关键词: 吐鲁番绿洲, 分类特征, 分类精度

Abstract:

Due to the presence of same object with different spectra and different objects with same spectrum, the accuracy of remote sensing classification is limited, which is especially obvious in arid region. Turpan Basin, which is the typical oasis-desert interlaced area in eastern Xinjiang, is selected to be the typical research area in this article. The data set in this study includes SPOT images with a resolution of 10 m, DEM and field data. At first, the classification system was built according to land use/cover characteristics of research area. Then common methods such as principal component transformation, tasseled cap transformation and minimum noise fraction transformation were used to extract spectral feature. Vegetation index and wetness index were also calculated based on SPOT image, and 12 spectral features were built using these methods. Finally a classification framework which contains 14 features was implemented by combining DEM and slope. Inter-class separability method was applied to choose the optimum feature combination. Based on spectral and spatial characteristics of different mixed ground objects, and combined with supervised classification results, a decision tree model was built to abstract the land use/cover information. The results showed that decision tree classification based on multi-parameters of land surface could make full use of terrain spectral information and spatial information, and distinguished confusion features effectively, such as different types of desertification land and urban construction land. Accuracy of the method is 88% and the kappa coefficient is 0.76. Its accuracy has been improved significantly compared with traditional classification methods, which have increased by 7%, 10% and 11% than maximum likelihood method, markov distance method and the minimum distance method. The whole process may provide reference for land use/cover real-time monitoring in oasis of arid areas, and it also has certain significance to desertification study. The novel of this article is that the classification system was built according to land use/cover characteristics of oasis. That is different type and different level desertification land was contained in classification system. So wind erosion desertification and salinization information were classified at the same time with other land types. It has certain significance for desertification prevention and control. Due to time restrictions, we only used traditional classification methods to classify spot raw data, and compared its accuracy with the results of multi-parameters decision classification. In the future studies, texture features and other features may be added, and different feature selection approaches can be adopted to compare the influence of different features on classification accuracy.

Key words: Turpan oasis, classification feature, classification accuracy

中图分类号: 

  • S127