Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data

By Bruno Aiazzi, Stefano Baronti, Massimo Selva. IEEE Transactions on Geoscience and Remote Sensing, 2007

1,161 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/be9232cec0d3ed27c584f4f3c78d09e418413592

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Citations in the corpus

Multi-source remote sensing data fusion: status and trends (2010, methodology): “…and Gram–Schmidt (GS) spectral sharpening (Laben and Brower 2000), also suffer from challenges on spectral preserving, and specific algorithms have also been developed to reduce the spectral distortion (Aiazzi et al. 2007, Shah et al. 2008) but these are less frequently used in operational work.”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “in 2007 proposed the general CS-based pansharpening framework, where various methods based on different transformation techniques can be explained in a unified way [48].”
A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement (2011, background): “generation turns out to be efficient in most cases [11].”
Hyperspectral Pansharpening: A Review (2015, methodology): “This process is expressed by (1) using the gains [37]”
A survey of classical methods and new trends in pansharpening of multispectral images (2011, background): “CS techniques are attractive because they are fast and easy to implement and allow users’ expectations to be fulfilled most of the time, since they provide pansharpened images with good visual/geometrical quality in most cases [50].”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “Close-ups of the main fusion results for the RR GE-1 Collazzone (selected bands: red, green, and blue): (a) GT, (b) EXP, (c) BT-H, (d) BDSD, (e) C-BDSD, (f) BDSD-PC, (g) C-GSA, (h) MTF-GLP-FS, (i) MTF-GLP-HPM-H, (j) MTF-GLP-HPM-R, (k) C-MTF-GLP-CBD, (l) MF, (m) FE-HPM, (n) SR-D, (o) PWMBF, (p) TV, (”
A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization (2014, methodology): “olor). Fig. 6. Hyperspectral + panchromatic fusion on dataset A. which may not be fulﬁlled when fusing panchromatic and hyperspectral images. The Gram-Schmidt adaptive (GSA) method from Aiazzi et al. [56] is an adaptation of the GramSchmidt spectral sharpening method (GS). The latter is based on the”
Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening (2014, background): “Unfortunately g is scarcely available in practice since it is typically neither provided by the sensors’ constructors nor derived analytically [11].”
High-resolution hyperspectral imaging via matrix factorization (2011, methodology): “To evaluate the performance of the proposed method, we implemented the following five methods: (1) Component substitution method [ 2 ], (2) Global PCA, (3) Local window, (4) RGB clustering methods (described in Sec.”
SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, methodology): “In [12], existing component substitution [13], [14], [15] and multiresolution analysis [16], [17], [18], [19], [20] methods designed for pansharpening are extended to super-resolve bands at 20 m of S2 images via integrating four fine bands into a single band.”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “like the advanced versions of GIHS and GS adopted in [7],”
Bayesian sparse representation for hyperspectral image super resolution (2015, background): “In order to show the performance difference between these methods and the other approaches mentioned in Section 2, we also report some results of the Component Substitution Method (CSM) [1], taken directly from [18].”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, methodology): “The most straightforward way to apply GSA to HS-MS data fusion is to construct multiple image sets for pansharpening subproblems where each set is composed of one MS band and corresponding HS bands grouped by correlationbased clustering.”