基于植被分区的多特征遥感智能分类  

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

作　　者：于菲菲[1,2] 曾永年[1,2] 徐艳艳[1,2] 郑忠[1,2] 刘朝松 王君[1,2] 何晋强[1,2] 

机构地区：[1]中南大学地球科学与信息物理学院,长沙410083 [2]中南大学空间信息技术与可持续发展研究中心,长沙410083

出　　处：《国土资源遥感》2014年第1期63-70,共8页Remote Sensing for Land & Resources

基　　金：国家自然科学基金项目(编号:41171326;41201383和41201386)资助

摘　　要：为了有效地提取大范围地形复杂区域的土地利用/土地覆盖遥感信息,以位居青藏高原与黄土高原过渡地带的青海东部地区为研究区,研究基于蚁群智能优化算法(ant colony intelligent optimization algorithm,ACIOA)的土地利用/土地覆盖遥感智能分类。首先选用TM图像、DEM、坡度和坡向数据作为分类的特征波段;然后利用归一化植被指数NDVI对实验区数据进行植被分区;最后利用ACIOA算法进行分类规则挖掘,并依据分类规则进行土地利用/覆盖信息的提取。研究表明,基于植被分区的多特征蚁群智能分类的总体精度为88.85%,Kappa=0.86,优于传统的遥感图像分类方法,为大范围地形复杂区域的土地利用/土地覆盖遥感信息提取提供了有效的方法。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 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.

关 键 词：蚁群智能优化算法(ACIOA) 植被分区 多特征 遥感分类 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置] S127[自动化与计算机技术—控制科学与工程]