Decision Tree Classification of Remotely Sensed Satellite Data using Spectral Separability Matrix

Abstract

In this paper an attempt has been made to develop a decision tree classification algorithm for remotely sensed satellite data using the separability matrix of the spectral distributions of probable classes in respective bands. The spectral distance between any two classes is calculated from the difference between the minimum spectral value of a class and maximum spectral value of its preceding class for a particular band. The decision tree is then constructed by recursively partitioning the spectral distribution in a Top-Down manner. Using the separability matrix, a threshold and a band will be chosen in order to partition the training set in an optimal manner. The classified image is compared with the image classified by using classical method Maximum Likelihood Classifier (MLC). The overall accuracy was found to be 98% using the Decision Tree method and 95% using the Maximum Likelihood method with kappa values 97% and 94 % respectively.