Fast, simple, and good pan-sharpening method

Abstract

Abstract.  Pan-sharpening of optical remote sensing multispectral imagery aims to include spatial information from a high-resolution image (high frequencies) into a low-resolution image (low frequencies) while preserving spectral properties of a low-resolution image. From a signal processing view, a general fusion filtering framework (GFF) can be formulated, which is very well suitable for a fusion of multiresolution and multisensor data such as optical-optical and optical-radar imagery. To reduce computation time, a simple and fast variant of GFF-high-pass filtering method (HPFM)—is proposed, which performs filtering in signal domain and thus avoids time-consuming FFT computations. A new joint quality measure based on the combination of two spectral and spatial measures was proposed for quality assessment by a proper normalization of the ranges of variables. Quality and speed of six pan-sharpening methods—component substitution (CS), Gram-Schmidt (GS) sharpening, Ehlers fusion, Amélioration de la Résolution Spatiale par Injection de Structures, GFF, and HPFM—were evaluated for WorldView-2 satellite remote sensing data. Experiments showed that the HPFM method outperforms all the fusion methods used in this study, even its parentage method GFF. Moreover, it is more than four times faster than GFF method and competitive with CS and GS methods in speed.