Fast, simple and good pan-sharpening method

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 multi-resolution and multi-sensor 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 (JQM) 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, ARSIS, GFF and HPFM was 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 4 times faster than GFF method and competitive with CS and GS methods in speed.