Intelligent remote sensing classification of multi-character data based on vegetation partition

Abstract

In order to effectively extract land use /land cover remote sensing information in a wide range of terrain complex area,the authors,taking the transition zone between Tibetan Plateau and the Loess Plateau in eastern Qinghai as the study area,studied the intelligent remote sensing classification of land use / land cover by using ant colony intelligent optimization algorithm( ACIOA) in this paper. Firstly,TM image,digital elevation model,slope and aspect data were selected as characteristic bands for classification. Secondly,the study area was divided into two parts using the normalized difference vegetation index( NDVI) so as to reduce the influence of different objects with the same spectrum. Finally,the classification rules were excavated using ACIOA,by which regional land use / cover information was extracted. The results show that the ACIOA classification of multi- character data based on vegetation partition is superior to the traditional remote sensing classification. The overall accuracy of the classification and the Kappa coefficient of ACIOA with multi- character data based on vegetation partition is 88. 85% and 0. 86 respectively. Therefore,this study provides an effective way for extracting land use / land cover information in large- area complex terrain.