Studying the effect of lossy compression and image fusion on image classification

Abstract

Nowadays, the remotely sensed images are of huge sizes that require the implementation of compression technique to be easily stored on the internet. In addition, image fusion which is the merging of panchromatic and multispectral images to generate a single image with high spatial and spectral resolutions is required to increase the information in the resulted image. The purpose of this paper is to study the effect of image compression and fusion techniques on the classification accuracy. In this study, two pan and mul Geo-eye images covering an area of Cape Town, South Africa were registered and fused using different fusion techniques. The fused image with the superior accuracy was then compressed with various compression ratios ranging from 1:10 to 1:100. Then, the compressed fused images were classified using Maximum Likelihood Classification and Artificial Neural Network Classification techniques. Finally, the confusion matrices of the classified images were generated and evaluated to determine the effect of compression and fusion techniques on the accuracy of the classification process. Keywords: Lossy compression, MrSid, Overall accuracy, HPF, RMSE