Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening

By Gemine Vivone, Rocco Restaino, M. Dalla Mura, Giorgio A. Licciardi, Jocelyn Chanussot. IEEE Geoscience and Remote Sensing Letters, 2014

365 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/40392ca0f1e42854ecb62d261ecf5dfa9095892c

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Citations in the corpus

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].”
Hyperspectral Pansharpening: A Review (2015, methodology): “In this case, the details are weighted by the ratio between the upsampled HS image and the low-pass filtered PAN one, in order to reproduce the local intensity contrast of the PAN image in the fused image [42].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “We compare five representative pansharpening algorithms, namely, Gram–Schmidt adaptive (GSA) [48], smoothing filtered-based intensity modulation (SFIM) [54], modulation-transfer-function-based generalized Laplacian pyramid with high-pass modulation (MTF-GLP-HPM) [76], ATPRK [27], and jointly sparse ”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, neutral): “BENCHMARK Several state-of-the-art algorithms are employed for comparison purposes: ◗ EXP: MS image interpolation using a polynomial kernel with 23 coefficients [43] ◗ CS methods • BT-H [59] • BDSD [33] • C-BDSD [34] • BDSD-PC [35] • GS [24] • GSA [30] • C-GSA [32] • PRACS [29] ◗ MRA methods • AWLP ”
A Critical Comparison Among Pansharpening Algorithms (2015, methodology): “However, some further improvements can be achieved by injecting the details using the HPM paradigm (16) [56].”
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] [”