An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets

By Vijay P. Shah, Nicolas H. Younan, Roger L. King. IEEE Transactions on Geoscience and Remote Sensing, 2008

672 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3a69bb41b1bc4bd4fc802e171a4c1f140f827b17

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, methodology): “CS methods [47, 48] apply a linear transformation on MS image to obtain a component that contains the degraded spatial information which is then substituted by the PAN image to inject the spatial information.”
Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band (2016, methodology): “PCA is an approach based on the component substitution for spectral transformation of the original data [48].”
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 Pan-Sharpening Method Using a Compressed Sensing Technique (2011, methodology): “The multiresolution transforms, such as discrete wavelet transform (DWT) [6], “à trous” wavelet transform (ATWT) [14], contourlets [15], and support value transform [16], are usually used to extract the high frequencies of the PAN image.”
Hyperspectral Pansharpening: A Review (2015, methodology): “[12] V.”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “Using independent component analysis in conjunction with curvelets [63] and using PCA in combination with contourlets [46] or guided filters [64] are other noticeable instances.”
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.”
Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, methodology): “Representative methods in pan-sharpening include the Intensity-Hue-Saturation (IHS) transform [22], PCA-based method [23], and compressed sensing based method [16].”
A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement (2011, methodology): “view of the expression of directional information and intrinsic geometrical structures, a combined adaptive PCA algorithm based on the discrete contourlet transform is developed [19].”
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 Critical Comparison Among Pansharpening Algorithms (2015, background): “In the same field, very appreciable results have been achieved by employing wavelet and contourlet decomposition pyramids [13], [66].”
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.”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “Spatial details can be extracted in different ways, using, for example, decimated or undecimated wavelet transforms [10]–[13], the Laplacian pyramids [14]–[18], or other nonseparable transforms, such as the contourlet [19].”
A survey of classical methods and new trends in pansharpening of multispectral images (2011, methodology): “To overcome this drawback, Shah et al.[53] proposed a new adaptive PCA-based pansharpening method that determines, using cross-correlation, the appropriate PC component to be substituted by the PAN image.”
A review of remote sensing image fusion methods (2016, methodology): “Multiresolution analysis (MRA) Decimated wavelet transform [54], [55] Discrete “wavelet packet” [70] Undecimated wavelet transform [58] À trous [57],[59] Laplacian Pyramid [60] Contourlet [61],[62] Multicontourlet [69] Curvelet [63],[64] Curvelet and contourlet [67] Ripplet [65],[66] Multiresolution”