Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data

By Jun Yu Li, José M. Bioucas-Dias. IEEE International Geoscience and Remote Sensing Symposium, 2008

434 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/6ba3b3b9122ab0e663af6186267a8c81a1dd661e

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. José M. Bioucas-Dias
2. Antonio J. Plaza
3. Jocelyn Chanussot
4. Sen Jia
5. Y. Qian
6. Jun Zhou
7. Antonio Robles-Kelly
8. Qian Du

In Ecology

1. Qian Du
2. Jocelyn Chanussot
3. Antonio J. Plaza
4. Paul D. Gader
5. Sen Jia
6. Antonio Robles-Kelly
7. Jun Zhou
8. Y. Qian

In Media Technology

1. José M. Bioucas-Dias
2. Antonio J. Plaza
3. Jocelyn Chanussot
4. Nicolas Gillis
5. Y. Qian
6. Sen Jia
7. Shutao Li
8. Paul D. Gader

Citations in the corpus

• Sparse Unmixing of Hyperspectral Data (2011, background): “…[18], iterated constrained endmembers (ICE) [19], minimum volume constrained non-negative matrix factorization [20], spatial–spectral endmember extraction [21], sparsity-promoting ICE [22], minimum volume simplex analysis [23], and simplex identification via split augmented Lagrangian [24].”
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, positive): “17, left, presents a scatterplot of the simulated data set SusgsP3SNRinfXSmix and the endmember estimates produced by VCA, MVES, MVSA, MVC-NMF, SISAL, and DECA algorithms.”
• Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, methodology): “Methods such as ICE [32], minimum-volume enclosing simplex [7], minimum-volume constrained NMF (MVC-NMF) [43], and SISAL [9] follow Craig’s unmixing criterion [6] that the end-member matrix A and its spanned simplex should enclose the observed pixels and minimize its volume.”
• A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, methodology): “Readers are referred to [2], [20], [36], and [40]–[43] for more numerical comparisons and real-data experiments.”
• A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, methodology): “This section presents results obtained with SISAL, MVSA (hard version) [15], MVES [14], and VeA [4] applied to simulated data sets.”