A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems
By Ben Somers, Stephanie Delalieux, J. Stuckens, W. Verstraeten, Pol R. Coppin. 2009
87 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Ecology, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/801bfa5920e08a4ad4e5f9e61ab44140ae2aac26
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
- Pedram Ghamisi
- Antonio J. Plaza
- Javier Plaza
- Jun Yu Li
- Xiuping Jia
- Philip E. Dennison
- Behnood Rasti
- Wenzhi Liao
In Ecology
- Dar A. Roberts
- Philip E. Dennison
- Antonio J. Plaza
- Pedram Ghamisi
- Kara N. Youngentob
- Javier Plaza
- Xiuping Jia
- Behnood Rasti
In Environmental Engineering
- Javier Plaza
- Antonio J. Plaza
- Xiuping Jia
- Alex Held
- Pedram Ghamisi
- Philip E. Dennison
- David B. Lindenmayer
- Kara N. Youngentob
Citations in the corpus
- Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “Another important concept in this category is the endmember bundle, explored by algorithms such as multiple endmember spectral mixture analysis (MESMA) [143].”
- Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data (2011, methodology): “In an attempt to overcome this limitation, various techniques to address endmember variability and similarity have been developed (e.g., Asner & Lobell, 2000; Bateson et al., 2000; Roberts et al., 1998; Somers et al., 2009a,b).”