Contextual techniques for classification of high and low resolution remote sensing data

Abstract

Conventional classification techniques, both supervised and non-supervised, are used for the classification of remote sensing data on the basis of spectral signatures of the classes of interest, and, contextual techniques use both spectral and spatial information to improve the classification accuracies.

This paper reviews some of the recent contextual classification techniques and proposes two methods. One method is meant for low resolution and the other for high resolution images. Test results for both the methods of two sets of data, corresponding to low resolution and high resolution, respectively, are compared with Gaussian Maximum Likelihood (GML) results and presented in the paper.