Paper Info
Title
Estimating the Number of Endmembers to Use in Spectral Unmixing of Hyperspectral Data with Collaborative Sparsity.
Abstract
Spectral Unmixing (SU) in hyperspectral remote sensing aims at recovering the signatures of the pure materials in the scene (end-members) and their abundances in each pixel of the image. The usual SU chain does not take spectral variability (SV) into account, and relies on the estimation of the Intrinsic Dimensionality (ID) of the data, related to the number of endmembers to use. However, the ID can be significantly overestimated in difficult scenarios, and sometimes does not correspond to the desired scale and application dependent number of endmembers. Spurious endmembers are then frequently included in the model. We propose an algorithm for SU incorporating SV, using collaborative sparsity to discard the least explicative endmembers in the whole image. We compute an algorithmic regularization path for this problem to select the optimal set of endmembers using a statistical criterion. Results on simulated and real data show the interest of the approach.
Year
DOI
Venue
2017
10.1007/978-3-319-53547-0_36
Lecture Notes in Computer Science
Keywords
Field
DocType
Hyperspectral images,Remote sensing,Collaborative sparsity,Alternating Direction Method of Multipliers,Regularization path,Bayesian Information Criterion
Bayesian information criterion,Pattern recognition,Curse of dimensionality,Hyperspectral imaging,Regularization (mathematics),Artificial intelligence,Pixel,Spurious relationship,Mathematics
Conference
Volume
ISSN
Citations 
10169
0302-9743
2
PageRank 
References 
Authors
0.36
10
4
Name
Order
Citations
PageRank
lucas drumetz1518.63
Guillaume Tochon2506.43
Jocelyn Chanussot34145272.11
Christian Jutten42925439.04