多时相影像的典型区农作物识别分类方法对比研究  

Study on Methods Comparison of Typical Remote Sensing Classification Based on Multi-temporal Images

作　　者：彭光雄[1] 宫阿都[2] 崔伟宏[1] 明涛[3] 陈锋锐[1] 

机构地区：[1]中国科学院遥感应用研究所,北京100101 [2]北京师范大学减灾与应急管理研究院,北京100875 [3]中国科学院地理科学与资源研究所,北京100101

出　　处：《地球信息科学》2009年第2期225-230,共6页Geo-information Science

基　　金：中国博士后科学基金项目(20080430586);国家自然科学基金(40801161、40701114);国际科技合作计划项目(2007DFA20640);中国科学院王宽诚博士后工作奖励基金

摘　　要：基于甘蔗和玉米的物候特征差异,对多时相影像典型分类方法处理的适宜性和准确性进行了比较研究。并以目视解译结果作为参考数据,利用全样本检验法,对自动分类的结果进行了精度检验。试验结果表明:面向对象法的分类精度最高,总体Kappa系数为0.655,是最适宜的方法;其次是BP神经网络法和光谱角制图法,总体Kappa系数分别为0.635和0.631;而最大似然法和分类后比较法则是不适宜采纳的分类方法,总体Kappa系数分别为0.601和0.577。上述分析可见,它们对遥感分类处理多时相影像识别算法的适用性选取有一定参考意义。The study area is located in Miller county, Yunnan province, China. An experiment to select the appropriate classification method for multi-temporal remote sensing images was done. Typical classification methods including Object-Oriented Classification（ OOC）, Back Propagation Neural Network （BPNN）, Spectral Angle Mapper（SAM）, Maximum Likelihood Classifier（MLC）, and Comparison After Classification（CAC） were tested in this experiment. In this study, using two-phase remote sensing images of CBERS02B-CCD and Landsat-5 TM, the suitability and accuracy of typical methods to deal with multi-temporal images classification were compared, based on different phenological characteristics of sugarcane, corn and paddy. Using full sample test method, visual interpretation results were used as reference data to validate the accuracy of different classification methods. The experimental results show that the order of overall classification accuracy from high to low is OOC, BPNN, SAM, MLC, and CAC, and the Kappa accuracy of them is 0. 655, 0. 635, 0. 631, 0. 601 and 0. 577, respectively. As it is easy to identify paddy, its accuracy is higher than that of sugarcane and corn. The order of accuracy of paddy for different methods is as the same as the order of overall accuracy, the highest and lowest accuracy of paddy is 0. 706 and 0. 621, respectively. The accuracy curve position between the accuracy of various land covers and the overall accuracy are consistent for MLC and CAC, and the overall accuracy of CAC is the lowest one. The accuracy of corn for OOC is the highest one with Kappa of 0. 611. The Kappa accuracy of sugarcane for OOC, SAM and BPNN is 0. 594, 0. 575 and 0. 575, respectively. In general speaking, for the remote sensing classification of Multi-temporal Images, OOC is the best, BPNN and SAM is better, MLC and CAC are the worst. The conclusions of this experiment have some guidance to select the appropriate classification method for multi-temporal remote sensing images.

关 键 词：多时相影像 遥感分类 方法比较 

分 类 号：S127[农业科学—农业基础科学] P237[天文地球—摄影测量与遥感]