Image quality assessment for fused remote sensing imageries

Abstract

Image fusion provides precise information in both spatial and spectral resolutions that benefit significantly in high accuracy mapping. Yet, there is less intention withdrawn in justifying the performance of the fused image. In this study, qualitative and quantitative assessments were carried out to test the quality of fusion image. Principal Component Analysis (PCA), Gram-Schmidt and Ehlers were applied to fuse the hyperspectral and Lidar image. Ehlers fusion showed good in preserving the color of image and contained the most information. Besides, the classification of Ehlers fused image showed the highest accuracy.