ICE: a statistical approach to identifying endmembers in hyperspectral images

By Mark Berman, Harri T. Kiiveri, Ryan Lagerstrom, Andreas T. Ernst, Rob Dunne, Jonathan F. Huntington. IEEE Transactions on Geoscience and Remote Sensing, 2004

433 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/0a17e44059fcbbe8a7282be7bd07f32314d4b420

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Citations in the corpus

Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization (2007, methodology): “Although the assumption of the presence of pure pixels improves the algorithm efficiency, in some cases, such as when the processing data are with low spatial resolution [12] or of specific ground covers [13], it is not reliable to make this assumption, especially for all endmember classes.”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “This is the case for the iterative constrained endmember (ICE) [144] and minimum volume constrained nonnegative matrix factorization approaches [145], the main differences of which are related to the way they define the data volume term.”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, background): “Note that the unconstrained BSS problem for estimating M and A from Y is ill-posed: if {Y,A} is an admissible estimate then { YH,HTA } is also admissible for any unitary matrix H.”
Sparse Unmixing of Hyperspectral Data (2011, methodology): “…convex cone analysis [16], iterative error analysis [17], automatic morphological endmember extraction [18], iterated constrained endmembers (ICE) [19], minimum volume constrained non-negative matrix factorization [20], spatial–spectral endmember extraction [21], sparsity-promoting ICE [22],…”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “The iterative constrained endmembers (ICE) algorithm [137] aims at solving an optimization problem similar to that of MVC-NMF, where the volume of the simplex is replaced by a much more manageable approximation: the sum of squared distances between all the simplex vertices.”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, background): “SPICE is arguably the first algorithm that explores such opportunity [73].”
Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, methodology): “In [31], sparsity-promoting priors are used with an extension of the iterated constrained end-member (ICE) algorithm [32] to determine the number of end-members, in which the sparsity is achieved by a zero-mean Laplacian distribution akin to the L1 regularizer.”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “This is the case of iterative constrained endmembers (ICE) algorithm [50] and of the minimum volume transform-nonnegative matrix factorization (MVC-NMF) [51], whose main differences are related with the way they define the regularizer M ( ) V .”