Image fusion—the ARSIS concept and some successful implementation schemes

By Thierry Ranchin, Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Lucien Wald. 2003

396 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5ba9eeb46f04eba62bb5a6c551386efe6296b19e

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods (2005, background): “Recently, more complex models have been proposed in [31] and [38].”
Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images (2008, methodology): “…(IHS) transformation or principal component analysis [20], the spectral distortion minimization (SDM) with respect to the resam-pled MS data [21], or the spatially adaptive injection as in the context-based-decision (CBD) algorithm [6] and in the method by Ranchin, Wald, and Mangolini (RWM) [22].”
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “Pansharpening related works can be regarded as special cases of HS-MS data fusion which aims to attain HS product with high spatial resolution by fusing paired HS-MS images.”
A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter (2006, background): “2) RASE and the ERGAS: To estimate the global spectral quality of the fused images, the index of the RASE is expressed as a percentage [3]–[5], [7], [8].”
A comparative analysis of image fusion methods (2005, methodology): “Currently used wavelet-based image fusion methods are mostly based on two computation algorithms: the Mallat algorithm [10], [18], [21]–[24] and the à trous algorithm [18], [25]–[28].”
Fusion of multispectral and panchromatic Satellite images using the curvelet transform (2005, methodology): “We based our analysis of the experimental results on the factors used in [7]–[10]; namely, the bias (Bias), the standard deviation (SD), and the correlation coefficient (CC, sCC).”
Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics (2008, background): “In all these formalisms, the fused MS product is a function of this linear combination and of the Pan image as well.”
A global quality measurement of pan-sharpened multispectral imagery (2004, neutral): “836784 data fusion have been found to benefit from an undecimated multiresolution analysis [8].”
Optimizing the High-Pass Filter Addition Technique for Image Fusion (2007, background): “A wide variety of metrics are presented in the literature (Wald et al., 1997; Nunez et al., 1999; Piella and Heijmans, 2003; Ranchin et al., 2003; Shi et al., 2003; Zhou et al., 1998).”
Remote sensing image fusion using the curvelet transform (2007, methodology): “This model is referred to in the literature [17] [18] [19] as an interband structure model (IBSM); it deals with the radiometric transformation (gain and offset) of spatial structures (edges and textures) when they are passed from Pan to MS images.”
Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data (2007, background): “of fused original images allows such global synthetic scores as Q4 [24], spectral angle mapper (SAM), and erreur relative globale adimensionnelle de synthése (ERGAS) [14], [16] to be”
A New Pan-Sharpening Method Using a Compressed Sensing Technique (2011, methodology): “In recent years, the methods based on the ARSIS concept have been popular which assume that the missing spatial information in the MS images can be obtained from the high frequencies of the PAN image [12], [13].”
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest (2007, methodology): “Multiresolution analysis (MRA) provides effective tools, like wavelets and Laplacian pyramids [2], [3], [18]–[20], to help carry out data-fusion tasks.”
Assessment of image fusion procedures using entropy, image quality, and multispectral classification (2008, methodology): “The advantage of using the PCS method is that an arbitrary number of bands can be used [21], whereas the IHS procedure is limited to only three bands.”
A survey of classical methods and new trends in pansharpening of multispectral images (2011, methodology): “It was found that many of the existing image fusion methods, such as the HPF and additive wavelet transform (AWT) methods, can be accommodated within the ARSIS concept [13], but Tu et al.”
Multi-source remote sensing data fusion: status and trends (2010, methodology): “Wald (2002) and Ranchin et al. (2003) introduce the ‘Ame´lioration de la Re´solution Spatiale par Injection de Structures’ (ARSIS, improving spatial resolution by structure injection) concept based on the assumption that the missing information is linked to the high frequencies of the data sets to…”
Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries (2013, background): “The correlation coefficient (CC) [32] and the root-mean-square error (RMSE) between the fused MS image and the reference MS image are”