基于最优波段组合的TM影像土地覆盖信息分类  

Land Cover Information Classification Based on the Optimal Band Combination of TM Image

作　　者：刘德儿[1] 于海霞[1] 兰小机[1] 陈元增 

机构地区：[1]江西理工大学建筑与测绘工程学院 [2]赣州市城市规划设计院

出　　处：《金属矿山》2013年第10期80-83,共4页Metal Mine

基　　金：国家自然科学基金项目(编号:40971234);江西省教育厅科学研究项目(编号:GJJ13431);江西理工大学基金项目(编号:jxxj11012)

摘　　要：针对TM遥感影像光谱特征利用率不高,影响土地覆盖信息分类精度的问题,提出一种基于最优波段组合的分类方法。以赣州市章贡区2006年的TM遥感影像为研究对象,首先根据遥感影像的光谱特征和波段间的相关性计算最佳指数;其次根据研究区域特征,引入修正植被指数,并对原影像进行主成分分析,综合分析最佳指数、修正植被指数和前3个主成分量,认为PC3、RNDVI、Band1为最优波段组合。最后结合监督分类与非监督分类法对最优波段组合成的遥感影像进行分类,得到的整体分类精度为86.237 5%,kappa系数为0.825 3。The accuracy of land cover classification is affected by the low utilization efficiency of the TM image spec- tral characteristics. Aimed at this issue, a classification method is proposed based on the optimal band combination. Taking the TM image in 2006 in Zhanggong district of Ganzhou as a study goal, the optimum index factor（OIF） is firstly calculated by the spectral characteristics of remote sensing image and the relation of bands. Then, the revised normalized difference vegetation index（RNDVI） is introduced according to the topographic feature in study region, and the original image is ana- lyzed by principal component analysis （PCA）. Through comprehensive analysis on the optimum index factor, the revised nor- malized difference vegetation index and three former principal components, it is concluded that the optimal band combina- tions are PC3, RNDVI and Bandl. Finally, with combination of supervised classification and unsupervised classification, the composite remote sensing image is classified ,obtaining that the overall classification accuracy is 86. 2375% and the kappa factor is 0. 8253.

关 键 词：最优波段组合 最佳指数 修正植被指数 主成分分析 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]