Improvement of Classification Accuracy Using Image Fusion Techniques

Abstract

Remote sensing techniques have been widely used for identification of land use and land cover features. Land information can be easily collected by classification of satellite images in the context of their use. In this paper study area has been classified into three classes i.e. settlement, trees and agricultural by classification of an image which has been enhanced using fusion of two images. The spatial and spectral resolutions of different satellite images provide better information with the aid of initial processing of image and fusion of both images. The satellite images fused together are multispectral IRS-P6 also called Resourcesat-1 satellite, on board LISS-III sensor provide image with spatial resolution of 23.5 m and an IRS-P5 also called Cartosat-1 satellite provides single band panchromatic image with spatial resolution of 2.5 m. Erdas Imagine 9.1 software has been used for image processing, fusion and supervised classification of the images. The Brovery, Multiplicative and Principal Component Analysis (PCA) method have been used for image fusion. The resultant images have been classified using the supervised classification with maximum likelihood parametric rule for information extraction and comparison between them in terms of their accuracy.