The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper

Image fusion is a useful tool in integrating a high-resolution panchromatic image (HRPI) with a low-resolution multispectral image (LRMI) to produce a high-resolution multispectral image (HRMI). To date, many image fusion techniques have been developed to try to improve the spatial resolution of the LRMI to that of the HRPI with its spectral property reliably preserved. However, many studies have indicated that there exists a trade- off between the spatial resolution improvement and the spectral property preservation of the LRMI, and it is difficult for the existing methods to do the best in both aspects. Based on one minimization problem, this paper mathematically analyzes the tradeoff in fusing remote sensing images. In experiment, four fusion methods are evaluated through expanded spectral angle mapper (ESAM). Results clearly prove that all the tested methods have this property.