决策树在遥感影像分类中的应用

在充分了解决策二叉树分类过程的基础上, 以西藏波密地区的TM遥感影像为研究对象, 提取并分析各种典型地物的反射光谱特征, 利用不同地物的光谱差异并结合人工目视判读建立了一组能够精确、快速划分遥感影像的分类规则。用ENVI建立分类决策树对实验区遥感影像进行分类, 并将分类结果和传统的最大似然法相比较。结果表明:决策树分类的总体精度和Kappa分别为97.15%和0.9644, 优于传统的最大似然法, 取得了较为满意的分类结果。

Decision Tree and Its Application in Remote Sensing Image Classification

Taking TM images in Bomi county of Tibet as the object to study, choosing and analyzing the reflect spectral characteristics of typical land-cover types, a set of accurately and fast rules for TM images classification has been established based on spectral characteristics diversity of different land-cover types and artificial visual interpretation.The classification decision tree was applied to classify the TM images in experimental area, and the classification results were compared with traditional maximum likelihood.The results show that: the overall accuracy and Kappa coefficient of decision tree reach 97.15% and 0.9644, respectively, whose performance is more excellent than the traditional maximum likelihood method, and obtain a more satisfactory classification.