Spatial/spectral endmember extraction by multidimensional morphological operations

By Antonio J. Plaza, Pablo Martínez, Rosa M. Pérez, Javier Plaza. IEEE Transactions on Geoscience and Remote Sensing, 2002

593 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/273ebdbefd2b0d9653491fed7bb7fb9c645a7171

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Citations in the corpus

A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, methodology): “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].”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, methodology): “The algorithm, based on the previously developed automated morphological endmember extraction (AMEE) algorithm (Plaza et al., 2002), is called I-AMEE to account for the new iterative nature of the proposed algorithm.”
A Review of Nonlinear Hyperspectral Unmixing Methods (2014, methodology): “First, endmembers are estimated with the automated morphological endmember extraction [130] algorithm.”
Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors (2008, methodology): “the image using the Automated Morphological Endmember Extraction (AMEE) method [39], [40].”
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing (2012, background): “Previous works showed that including spatial information, both as a preprocessing step [54] and in the unmixing procedure itself [55], [56], has a positive impact on the accuracy of the estimated fractional abundances.”
Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, unknown): “It takes roots in the inspiring works of [31] to compute end-members by beneﬁting from the correlation between spatially close pixels and [32] by exploiting a mixture of spatial and spectral classiﬁers.”
Sparse Unmixing of Hyperspectral Data (2011, methodology): “…spectral identiﬁcation systems [15], 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…”
A New Growing Method for Simplex-Based Endmember Extraction Algorithm (2006, unknown): “Many algorithms have been developed for this purpose, such as the pixel purity index (PPI) [2], N-finder algorithm (N-FINDR) [3], iterative error analysis (IEA) [4], automated morphological endmember extraction (AMEE) algorithm [5], minimum volume transform (MVT) [6], convex geometry [7], convex con”
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations (2005, methodology): “Our previous research [11], [12] has examined the application of multichannel mathematical morphology (MM) opera-”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “Automatic morphological endmember extraction [152] uses extend ed morphological transformations to integrate spatial and spectral information.”
Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network (2017, background): “Such analysis can be fulfilled by extracting endmembers [9,10] and estimating their fractional abundance [11] separately, or treating these two problems simultaneously as a blind signal decomposition problem, for which non-negative matrix factorization (NMF) [12,13], convex optimization [14], and Ne”
A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data (2004, methodology): “The spectrally purest and the spectrally most highly mixed spectral signatures are respectively obtained at the neighborhood of defined by using the following extended morphological operations [18]:”
Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016, background): “Based on these two aspects of issues, it is commonly recognized that both effective dimension reduction (DR) methods and robust spatial feature extraction methods should be proposed [5].”
Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing (2010, unknown): “Therefore, ﬁnding the endmembers amounts to identifying the vertices of that simplex [1, 2, 19, 17, 4, 14, 21].”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “Extended morphological operations have been also used as a baseline to develop an automatic morphological endmember extraction (AMEE) algorithm [208] for spatial-spectral endmember extraction.”
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 Automated Morphological Endmember Extraction (AMEE) [12], the vertex component analysis (VCA) [4], and the simplex growing algorithm (SGA) [13].”