Endmember variability in Spectral Mixture Analysis: A review

By Ben Somers, Gregory P. Asner, Laurent Tits, Pol R. Coppin. 2011

687 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/a0fdf254ba624a3f90776f6f51d791cd415cb387

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Jocelyn Chanussot
2. Danfeng Hong
3. Paul D. Gader
4. Mario Parente
5. Yunhao Chen
6. Ji Zhou
7. Jinfei Wang
8. Rob Heylen

In Ecology

1. Keely L. Roth
2. Dar A. Roberts
3. Philip E. Dennison
4. Danfeng Hong
5. Jocelyn Chanussot
6. Rob Heylen
7. Paul D. Gader
8. Darius Phiri

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Keely L. Roth
4. Dar A. Roberts
5. Philip E. Dennison
6. Naoto Yokoya
7. Xiaoxiang Zhu
8. Paul D. Gader

Citations in the corpus

• Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. (2012, methodology): “This method is similar to the idea of endmember selection (Bateson et al. 2000; Somers et al. 2011).”
• Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data (2012, methodology): “Somers et al. (2011) provide an extensive review of these techniques, along with others which incorporate spectral feature selection (e.g., PCA-based SMA, Stable Zone Unmixing), spectral weighting, spectral transformations (e.g., normalized SMA, Derivative Spectral Unmixing) and spectral modeling…”
• Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “This method was developed in the early 1980s (Figure 2) and has been applied extensively on Landsat land cover classification [63,66,67].”
• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing (2018, background): “Spectral variability refers to a variation of a spectral signature for a given material, due to illumination conditions and topography, atmospheric effects, or even the intrinsic variability of the material [15] [16].”
• Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats (2013, result): “This conclusion is supported by studies in optical SMA in which the additivity (whether linear or nonlinear) of endmembers remains debatable (Somers et al., 2011).”
• A Review of Nonlinear Hyperspectral Unmixing Methods (2014, methodology): “We refer to the tutorial [8] for a general introduction to the unmixing problem, and to [9], [12] for recent and extensive overviews of linear unmixing techniques.”