Performance evaluation of different references based image fusion quality metrics for quality assessment of remote sensing Image fusion

Abstract

This paper focus on eight frequently used Image fusion quality metrics (IFQMs), which are correlation coefficient, relative bias, structure similarity index, root-mean-square error, cross entropy, mutual information, spectral angle mapper and ERGAS to check their ability to measure the quality similarity among the images based on three different reference images. We have evaluated the performance of the IFQMs by considering in the three aspects: (1) Consistency with the other similar IFQMs; (2) Robustness to different testing images; (3) Consistency with the visual evaluations. Experimental results show that taking resampled multispectral image as reference, ERGAS outperforms other IFQMs. Taking original low resolution multi-spectral image as reference, correlation coefficient and SAM have the best performance. Taking the high resolution pan or SAR image as reference, root-mean-square error and ERGAS perform well. Relative bias is not suitable for fusion image evaluation due to its poor performance in all the three aspects.