A Review of Nonlinear Hyperspectral Unmixing Methods
By Rob Heylen, Mario Parente, Paul D. Gader. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
539 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Media Technology
View Paper: https://www.semanticscholar.org/paper/29a2c2a5ee6ede5e1a83b6d10aa6e5d9d717e606
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
- Antonio J. Plaza
- Javier Plaza
- Pedram Ghamisi
- Juan Mario Haut
- Mercedes Eugenia Paoletti
- Jun Yu Li
- Behnood Rasti
- Wenzhi Liao
In Media Technology
- Antonio J. Plaza
- Javier Plaza
- Juan Mario Haut
- Pedram Ghamisi
- Mercedes Eugenia Paoletti
- Jun Yu Li
- Behnood Rasti
- Naoto Yokoya
Citations in the corpus
- Deep learning classifiers for hyperspectral imaging: A review (2019, background): “The most popular ones include: (i) spectral unmixing (Bioucas-Dias et al., 2012; Heylen et al., 2014; Shi and Wang, 2014; Sánchez et al., 2015; Zhong et al., 2016a), (ii) resolution enhancement (Eismann and Hardie, 2005; Mookambiga and Gomathi, 2016; Yi et al.”
- Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “However, intimate mixtures happen at microscopic scales, thus complicating the analysis with nonlinear mixing effects [133].”