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

Image fusion is a useful tool in integrating a high-resolution panchromaticimage (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolutionmultispectral image (HRMI). To date, many image fusion techniques have beendeveloped to try to improve the spatial resolution of the LRMI to that of the HRPI with itsspectral property reliably preserved. However, many studies have indicated that thereexists a trade- off between the spatial resolution improvement and the spectral propertypreservation of the LRMI, and it is difficult for the existing methods to do the best in bothaspects. Based on one minimization problem, this paper mathematically analyzes thetradeoff in fusing remote sensing images. In experiment, four fusion methods are evaluatedthrough expanded spectral angle mapper (ESAM). Results clearly prove that all the testedmethods have this property.