Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation

By Thierry Ranchin, Lucien Wald. 2000

935 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ae3cf7f2e2a9afbcee463d9666ab344bc118f14d

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Citations in the corpus

A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement (2011, background): “Based on this hypothesis, various statistical evaluation indicators of spatially degraded data, such as correlation coefficient, erreur relative globale adimensionnelle de synthese, spectral angle mapper, and Q4 index, are proposed [20], [37]– [40].”
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”
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.”
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].”
Pansharpening by Convolutional Neural Networks (2016, methodology): “Other methods following the same philosophy work in a MRA setting, e.g., ATWT [15,19–21] or with LP representations [16,22–24].”
Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images (2008, methodology): “…step is deﬁned within the Amélioration de la Ré-solution Spatiale par Injection de Structures (ARSIS) concept as interband structure model (IBSM) [10]. b) the adaptation at the high resolution of this mathematical relationship in order to transform the high-resolution details of Pan…”
Fusion of multispectral and panchromatic Satellite images using the curvelet transform (2005, background): “ARSIS is based on the assumption that missing information is linked to the high frequencies of the Pan and MS images [7], [8].”
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest (2007, methodology): “In2000, Ranchin and Wald [2] proposed an error index that offers a global picture of the quality of the fused product.”
Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening (2014, methodology): “A more sophisticated way for building the PLP image consists in utilizing a multiresolution analysis (MRA) [13], which aim at separating the informative content at the various spatial”
Image fusion—the ARSIS concept and some successful implementation schemes (2003, methodology): “Ranchin and Wald (2000a) distinguish three groups of methods: the projection and substitution methods, the relative spectral contribution methods and those relevant to the ARSIS (from its French acronym: “Amélioration de la Résolution Spatiale par Injection de Structures”, which means “spatial…”
A survey of classical methods and new trends in pansharpening of multispectral images (2011, methodology): “Since this image is not available, Wald et al.[67] proposed a protocol for quality assessment and several quantitative measures for testing the three properties.”
Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition (2004, methodology): “To evaluate this spatial detail addition, we used the procedure proposed by Zhou [12].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, unknown): “[45] T.”
Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, methodology): “Previous researches such as ARSIS concept methods [9, 12] believe the spatial information lies in the high frequency of the PAN images.”
A Critical Comparison Among Pansharpening Algorithms (2015, methodology): “By focusing on the widely diffused wavelet decompositions , the decimated Mallat's method has been initially used for this purpose [54], [67].”
SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, unknown): “In[12],existingcomponent substitution[13],[14],[15]andmultiresolutionanalysis[16], [17],[18],[19],[20]methodsdesignedforpansharpeningare extendedtosuper-resolvebandsat20mofS2imagesvia integratingfourﬁnebandsintoasingleband.”
Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods (2005, methodology): “—injection directly into each LRM band, [26]–[31]; —injection into each LRM band through an IHS transform, [33]–[36]; —injection into each LRM band through a principal component analysis [32], [33], [36].”