Image fusion—the ARSIS concept and some successful implementation schemes

Abstract

This article aims at explaining the ARSIS concept. By fusing two sets of images A and B, one with a high spatial resolution, the other with a low spatial resolution and different spectral bands, the ARSIS concept permits to synthesise the dataset B at the resolution of A that is as close as possible to reality. It is based on the assumption that the missing information is linked to the high frequencies in the sets A and B. It searches a relationship between the high frequencies in the multispectral set B and the set A and models this relationship. The general problem for the synthesis is presented first. The general properties of the fused product are given. Then, the ARSIS concept is discussed. The general scheme for the implementation of a method belonging to this concept is presented. Then, this article intends to help practitioners and researchers to better understand this concept through practical details about

QuickBird‐based analysis for the spatial characterization of archaeological sites: Case study of the Monte Serico medieval village

“…It is expected that the use of QuickBird panchromatic image should be able to detect the typical marks expected in the presence of buried structures as in the case of aerial photos. The main advantages compared to aerial photos, are the synoptic view and, above all, the possibility of performing a combined analysis with multispectral images by using data fusion algorithms whose benefits have been assessed in a number of different application fields [see, e.g., Ranchin et al, 2003]. In our case, the data fusion was performed by using an algorithm specifically developed for VHR satellite images and based on least square method [Zhang, 2002].…”

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

“…The MRA-based fusion techniques adopt multi-resolution analysis methods, such as multi-resolution wavelet [5,[49][50][51][52][53] and the Laplacian pyramid [54,55], to decompose multispectral and panchromatic images with different levels. They subsequently derive the spatial details that are imported into the finer scales of the multispectral images with respect to the relationship between the panchromatic and multispectral images in the coarser scales [5,54,56,57].…”

“…They subsequently derive the spatial details that are imported into the finer scales of the multispectral images with respect to the relationship between the panchromatic and multispectral images in the coarser scales [5,54,56,57]. The typical fusion algorithms based on MRA include the à trous wavelet image fusion algorithm which adopts translation invariant and undecimated wavelet transform technique [50,58], and the fusion algorithm based on the generalized Laplacian pyramid (GLP) [54].…”

“…Shettigara [2] presented a generalized component substitution technique. The "Amélioration de la Résolution Spatiale par Injection de Structures" (ARSIS, improving spatial resolution by structure injection) concept is based on the assumption that the missing information is linked to the high frequencies of the datasets to be fused [5]. PCA, Brovey and wavelet transform fusion techniques are evaluated and found to be IHS-like image merging techniques [75].…”

“…Thus far, many pan-sharpening methods [2][3][4][5][6][7][8] have been presented in which the structural and textural details of the lower resolution multispectral image are enhanced by adopting the higher resolution panchromatic image corresponding to the multispectral image. Thus, the key issues of pan-sharpening concentrate on maximum enhancement of the spatial details at the expense of the minimum spectral distortion derived from the fusion operations.…”