A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement

By Jaewan Choi, Kiyun Yu, Yongil Kim. IEEE Transactions on Geoscience and Remote Sensing, 2011

548 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/87df6bfe596dd0edd434aab71cc8f3bff90e2e47

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In Computer Vision and Pattern Recognition

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Citations in the corpus

A Critical Comparison Among Pansharpening Algorithms (2015, methodology): “A new formalization of the CS approach was proposed by Tu et al. [44] and then analyzed in subsequent works [43], [45]–[47].”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “Besides our CNN-based methods, we consider a number of well-known conventional techniques selected because of their good performance, and specifically, PRACS [8], Indusion [13], AWLP [12], ATWT-M3 [10], MTF-GLP-HPM [15], BDSD [9], and its recent extension C-BDSD [22].”
Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening (2021, methodology): “the MS image interpolation using a polynomial kernel with 23 coefficients (EXP ) [17], the GS sharpening approach [12], the SFIM [13], the PRACS approach [11], the BDSD method [9], the robust BDSD approach (BDSD-PC) [10], the GLP with MTF-matched filter [58] and multiplicative injection model [59] [”
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.”
A review of remote sensing image fusion methods (2016, methodology): “there is another formalization which is proposed and analyzed in [52] and [53].”
PanNet: A Deep Network Architecture for Pan-Sharpening (2017, methodology): “We compare with six widely used pan-sharpening methods: PRACS [8], Indusion [19], PHLP [18], BDSD [13], SIRF [6, 7] and PNN [21].”
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening (2017, methodology): “In contrast, better colors are obtained in the results of PRACS [5],”
Pansharpening by Convolutional Neural Networks (2016, methodology): “In [9] two enhanced versions of GIHS and GS are proposed to deal with this problem, while in [10] an adaptive approach using partial replacement (PRACS) is presented.”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, methodology): “PARTIAL REPLACEMENT ADAPTIVE COMPONENT SUBSTITUTION The concept of partial replacement of the intensity component is introduced in [29].”
Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018, methodology): “For a few representative examples we refer to (Choi et al., 2011), (Lee and Lee, 2010) and (Garzelli et al.”
Pixel-level image fusion: A survey of the state of the art (2017, background): “In order to solve this problem, some researchers aim at estimating the optimal component to be substituted adaptively[76, 77, 80].”