A new look at IHS-like image fusion methods

By T. Tu, S. Su, H. Shyu, P. Huang. 2001

977 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

A review of remote sensing image fusion methods (2016, background): “Component substitution (CS) IHS and different versions of it [39],[42],[43] Fast IHS [44],[45],[46] Generalized IHS [47],[48],[49] Brovey transform (BT) [40] PCA [49],[50],[51] Gram-Schmidt (GS) [41]”
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, methodology): “Note that, instead of modifying the principal components directly as in the substitution-based method [39], [40], the PCA is only employed to learn the subspace where the fusion problem is solved.”
A survey of classical methods and new trends in pansharpening of multispectral images (2011, methodology): “Tu et al.[47] proposed a generalized IHS transform that surpasses the dimensional limitation.”
Hypercomplex Quality Assessment of Multi/Hyperspectral Images (2009, methodology): “Three fusion methods were applied to the considered data set: the generalized intensity–hue– saturation (GIHS) method [13], the Gram–Schmidt (GS) spectral sharpening method [14], and the modified high-pass”
Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, methodology): “The PCA based [4, 5], the IHS based [6, 7] and the Gram-Schmidt (GS) transform [8] based methods are those of the most widely known CS methods.”
Hyperspectral Pansharpening: A Review (2015, methodology): “The main advantages of the CS-based fusion techniques are the following: i) high fidelity in rendering the spatial details in the final image [37], ii) fast and easy implementation [8], and iii) robustness to misregistration errors and aliasing [38].”
A Sparse Image Fusion Algorithm With Application to Pan-Sharpening (2013, methodology): “The reconstructed HR multispectral images are compared with the results produced by the original IHS, adaptive IHS, PCA, and Brovey transform methods in Table V.”
A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter (2006, methodology): “Indeed, this difference (Pan-I) causes the altered saturation component in the RGB-IHS conversion model [18].”
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest (2007, background): “This procedure is equivalent to inject, i.e., add, the difference between the sharp Pan and the smooth intensity I into the resampled MS bands [14].”
Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening (2014, methodology): “Furthermore, the FIHS method employs (10) for the injection of details [12].”
Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics (2008, background): “[14], [15] pioneered a new way of tackling the linear IHS formulation discussed in [13].”
A New Pan-Sharpening Method With Deep Neural Networks (2015, methodology): “Initial methods are the case of the componentsubstitution methods, which mainly include the intensity– hue–saturation (IHS) [2], [3], the principal component analysis (PCA) [4], and the Gram–Schmidt (GS) transform [5]-based methods.”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, background): “Under the hypotheses of linear transformation and the substitution of a sole component, the CS fusion process can be strongly simplified without the explicit calculation of forward and backward transformations [123].”
A global quality measurement of pan-sharpened multispectral imagery (2004, background): “The undesired result is spectral distortion appearing in color composite displays [10].”