A New Growing Method for Simplex-Based Endmember Extraction Algorithm

By Chein-I. Chang, Chao-Cheng Wu, Weimin Liu, Yen Chieh Ouyang. IEEE Transactions on Geoscience and Remote Sensing, 2006

505 citations recorded by the source corpus.

Subfields: Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/63e0f6a51f7644d39c824127a44b2f619e4130f9

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In Media Technology

1. Antonio J. Plaza
2. José M. Bioucas-Dias
3. Jun Yu Li
4. Jocelyn Chanussot
5. Jon Atli Benediktsson
6. Shutao Li
7. Nasser M. Nasrabadi
8. Gustau Camps-Valls

In Ocean Engineering

1. Jun Yu Li
2. Antonio J. Plaza
3. José M. Bioucas-Dias
4. Jocelyn Chanussot

Citations in the corpus

• Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “Representative algorithms of class a) are pixel purity index (PPI) [43], vertex component analysis (VCA) [44], simplex growing algorithm (SGA) [45] successive volume maximization (SVMAX) [46], and the recursive algorithm for separable NMF (RSSNMF) [47]; Representative algorithms of class b) are N-FI”
• Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data (2008, methodology): “Some popular algorithms taking this assumption are the the N-FINDR [10], the the pixel purity index (PPI) [11] , the vertex component analysis (VCA) [4], and the simplex growing algorithm (SGA) [13].”
• Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization (2010, background): “The nBSS based on the pure-source sample assumption (where a pure-source sample means a data point that is fully dominated by only one source) has been investigated for separation of dependent sources in biomedical imaging [21], [22], [23] and remote sensing applications [24], [25], [26].”
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “• The simplex growing algorithm (SGA) [124] iteratively grows a simplex by finding the vertices corresponding to the maximum volume.”
• A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, background): “Some popular algorithms taking this assumption are the the N-FINDR [10], the thepixel purity index(PPI) [11], theautomated morphological endmember extraction(AMEE) [12], thevertex component analysis(VCA) [4], and thesimplex growing algorithm (SGA) [13].”