Hyperspectral Super-Resolution by Coupled Spectral Unmixing
By Charis Lanaras, Emmanuel Baltsavias, Konrad Schindler. IEEE International Conference on Computer Vision, 2015
375 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/19c49718f05347800b18c1276b285e1e4a1b942c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Leyuan Fang
- Shutao Li
- Jocelyn Chanussot
- Jon Atli Benediktsson
- Pedram Ghamisi
- Renwei Dian
- Naoto Yokoya
- Lorenzo Bruzzone
In Media Technology
- Shutao Li
- Leyuan Fang
- Jon Atli Benediktsson
- Jocelyn Chanussot
- Pedram Ghamisi
- Lorenzo Bruzzone
- Renwei Dian
- Francesca Bovolo
Citations in the corpus
- Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, methodology): “Instead of estimating spectral basis in advance and keeping it fixed, non-negative coupled matrix factorization methods [38, 15] are utilized to unmix both the LR-HSI and HR-MSI simultaneously.”
- Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, methodology): “The algorithm requires the availability of the relative SRF. F. ICCV’15 Similar to CNMF, Lanaras’s algorithm [54] jointly unmixes the two input images into the spectral signatures of endmembers and the associated fractional abundances.”
- Deep Hyperspectral Image Sharpening (2018, neutral): “We have compared the proposed DHSIS method with three recent state-of-the-art HSI sharpening methods: the coupled spectral unmixing (CSU) [37], the nonnegative-structured”
- Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, methodology): “We compare the LTTR method with three current state-of-the-art HSI super-resolution approaches, including non-negative structured sparse representation (NSSR) [33], coupled spectral unmixing (CSU) [28], and nonlocal sparse tensor factorization method (NLSTF) [35].”
- Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “This category of techniques includes various methods based on Bayesian probability [68], matrix factorization [66], and spectral unmixing [75].”