multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have

This paper presents a novel method for the spatial quality im-provement of low resolution Hyperspectral (HS) images by making use of a high resolution panchromatic (Pan) image. Since the introduction of all the details extracted from the Pan image into the upscaled HS images may result in spectral

Where, fI represents the pansharpened high resolution HS image, fI represents the upscaled low resolution HS im-age, P represents the Pan details and a represents the amount of details that should be added to improve spatial resolution of the scene. Thus, the goal of pansharpening is to add

Abstract not found

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 out-performs all the fusion methods

Quality Without Reference (QNR) index can be used to glob-ally assess the quality of pansharpened images without the need of a reference high resolution multispectral (MS) image. The QNR index relies on local calculation of the Q4 index. Exploiting the local Q4 calculation property of the QNR in

. The methods are the generalized Intensity-Hue-Saturation method, the princi-ple component substitution method, and Gram-Schmidt method

Pansharpening is generally defined as a technique that is used to combine the spatial structure of a high resolution panchromatic image with the spectral information of a lower resolution multispectral image to produce a high resolution multispectral image. In this study, image data of the new

focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: 1) a low spatial resolution multispectral image and 2) a high spatial resolution panchromatic image. We introduce

of the pansharpened images, we observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to design a method taking advantage of both methods by locally selecting the best one. This adaptive decision fusion is performed based on the local scale