Exploring the optimal combination of image fusion and classification techniques

Abstract

This study explores an optimal combination of certain selected image fusion and image classification techniques for improvement in classification accuracy. For this purpose, Sentinel-1A SAR and Sentinel-2A multispectral (MS) data have been obtained. Four fusion techniques Brovey Transform, Hue-Saturation-Value (HSV), PCA, and Gram-Schmidt (GS) Transform, have been compared along with two classification methods, Support Vector Machine (SVM) and Maximum Likelihood (ML) have been explored. Improvement in five classes Urban, Water, Baresoil, Forest and Vegetation Agricultural has been analysed. The result indicates that the GS-ML combination (87.56%) gives superior classification accuracy than the GS-SVM combination (84.35%). In contrast, PCA in combination with SVM (82.62%) provides superior accuracy compared to the combination of PCA-ML (82.56%).