Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details
By Jason Liu, E. Basaeed. 2000
409 citations recorded by the source corpus.
Subfields: Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/604d13e959149434e1c39873d73e0a191f4a502c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Lorenzo Bruzzone
- Francesca Bovolo
- Jon Atli Benediktsson
- Antonio J. Plaza
- Pedram Ghamisi
- Sicong Liu
- Javier Plaza
- Jun Yu Li
In Media Technology
- Antonio J. Plaza
- Jon Atli Benediktsson
- Pedram Ghamisi
- Lorenzo Bruzzone
- Jun Yu Li
- Javier Plaza
- Jocelyn Chanussot
- Shutao Li
Citations in the corpus
- Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “Representative MRA pan-sharpening algorithms include the smoothing filtered-based intensity modulation (SFIM) method [207], the additive wavelet luminance proportional method [211], and the generalized Laplacian pyramid (GLP) method [208].”
- Pansharpening by Convolutional Neural Networks (2016, neutral): “For example, in [17] a smoothing-filter-based intensity modulation (SFIM) has been proposed.”
- 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 ”