A New Technique for Multispectral and Panchromatic Image Fusion

AbstractIn this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transformation to the MS image to obtain the principal component (PC) images. A NSCT transformation to PAN and each PC images for N level of decomposition. We use FOCC as criterion to select PC. And then, we use the relative entropy as criterion to reconstruct high-frequency detailed images. Finally, we apply inverse NSCT to selected PC's low-frequency approximate image and reconstructed high- frequency detailed images to obtain high spatial resolution MS image. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance

A method to better account for modulation transfer functions in ARSIS-based pansharpening methods

International audienceMultispectral (MS) images provided by Earth observation satellites have generally a poor spatial resolution while panchromatic images (PAN) exhibit a spatial resolution two or four times better. Data fusion is a means to synthesize MS images at higher spatial resolution than original by exploiting the high spatial resolution of the PAN. This process is often called pansharpening. The synthesis property states that the synthesized MS images should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. The methods based on the concept Amélioration de la Résolution Spatiale par Injection de Structures (ARSIS) are able to deliver synthesized images with good spectral quality but whose geometrical quality can still be improved. We propose a more precise definition of the synthesis property in terms of geometry. Then, we present a method that takes explicitly into account the difference in modulation transfer function (MTF) between PAN and MS in the fusion process. This method is applied to an existing ARSIS-based fusion method, i.e., A trou wavelet transform-model 3. Simulated images of the sensors Pleiades and SPOT-5 are used to illustrate the performances of the approach. Although this paper is limited in methods and data, we observe a better restitution of the geometry and an improvement in all indices classically used in quality budget in pansharpening. We present also a means to assess the respect of the synthesis property from an MTF point of view

A modular platform for fusion of images

ISBN 2-912328-22-6 6 pagesInternational audienceThis paper presents a platform for the assessment of strategies for the fusion of images of various modalities and having different spatial resolution. The purpose of the fusion is the synthesis of multimodal images offering the best spatial resolution available in the data set. These synthesized images should be as close as possible to reality. This platform is written in IDL. It calls upon the ARSIS concept and thus comprises three categories of models. For each category, several models were implemented. Each model of a given category may be combined with models of the other categories, thus offering several possible strategies. The part « quality assessment » provides quantitative values for the fusion. It can also be executed, independently from the fusion part, on images resulting from a fusion process outside the platform. The platform should be made public

The ARSIS concept in image fusion: an answer to users needs

International audienceThe ARSIS concept (from its French name "amélioration de la résolution spatiale par injection de structures" that means improvement of the spatial resolution by structure injection) is briefly presented. It offers a framework for the synthesis of multi-modality images at highest spatial resolution by fusion of two sets of images. This concept is comprised of three types of models. These different types are introduced and a set of solutions for implementation proposed. Four solutions are detailed and applied to a satellite Ikonos image of the city of Hasselt, Belgium. The fusion products are analyzed visually and quantitatively. These analyses enhance the benefits of offering a set of solutions to remote sensing end-users in order to fulfill their needs

Data fusion: taking into account the modulation transfer function in ARSIS-based pansharpening methods

International audienceMultispectral images provided by satellite have a poor spatial resolution while panchromatic images (PAN) exhibit a spatial resolution two or four times better. Data fusion is a mean to synthesize MS images at higher spatial resolution than original by exploiting the high spatial resolution of the PAN. This process is often called pan-sharpening. The synthesized multispectral images should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. The methods based on the concept “Amélioration de la Résolution Spatiale par Injection de Structures” (ARSIS) concept are able to deliver synthesized images with good spectral quality but whose geometrical quality can still be enhanced. We propose to consider the characteristics of the sensor to improve the geometrical quality. We take explicitly into account the modulation transfer function (MTF) of the sensor in the fusion process. Though this study is limited in methods and data, we observe a better restitution of the geometry and an improvement in the majority of quality indices classically used in pan-sharpening. The communication also presents a means to assess the respect of the synthesis property from a MTF point of view

Image fusion and reconstruction of compressed data: A joint approach

International audienceIn the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources. The burden of data downlink is hence significantly reduced at the expense of a more laborious analysis done at the ground segment to estimate the missing information. The reconstruction algorithm estimates the target sharpened image directly instead of decompressing the original sources beforehand; a viable and practical novel solution is also introduced to show the effectiveness of the approach