Endmember Extraction Methods: A Short Review

The analysis of hyperspectral images on the basis of the spectral decomposition of their pixels through the so called spectral unmixing process, has applications in thematic map generation, target detection and unsupervised image segmentation. The critical step is the determination of the endmembers used as the references for the unmixing process. We give a comprehensive enumeration of the methods used in practice, because of its implementation in widely used software packages, and those published in the literature. We have structured the review according to the basic computational approach followed by the algorithms: those based on the computational geometry formulation, the ones following lattice computing ideas and heuristic approaches with a weak formal foundation.

[1]  Paul D. Gader,et al.  Sparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.

[2]  Antonio J. Plaza,et al.  Parallel Morphological Endmember Extraction Using Commodity Graphics Hardware , 2007, IEEE Geoscience and Remote Sensing Letters.

[3]  Maurice D. Craig,et al.  Minimum-volume transforms for remotely sensed data , 1994, IEEE Trans. Geosci. Remote. Sens..

[4]  Derek Rogge,et al.  Integration of spatial–spectral information for the improved extraction of endmembers , 2007 .

[5]  Jing Wang,et al.  Applications of Independent Component Analysis in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[6]  José M. Bioucas-Dias,et al.  Does independent component analysis play a role in unmixing hyperspectral data? , 2005, IEEE Trans. Geosci. Remote. Sens..

[7]  J. Boardman,et al.  Mapping target signatures via partial unmixing of AVIRIS data: in Summaries , 1995 .

[8]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Antonio J. Plaza,et al.  Spatial/spectral endmember extraction by multidimensional morphological operations , 2002, IEEE Trans. Geosci. Remote. Sens..

[10]  Manuel Graña,et al.  Associative morphological memories for endmember determination in spectral unmixing , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[11]  Antonio J. Plaza,et al.  Impact of Initialization on Design of Endmember Extraction Algorithms , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Antonio J. Plaza,et al.  A fast iterative algorithm for implementation of pixel purity index , 2006, IEEE Geoscience and Remote Sensing Letters.

[13]  Manuel Graña,et al.  Hyperspectral image analysis with associative morphological memories , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[14]  M. Grana,et al.  Associative morphological memories for endmember induction , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[15]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[16]  Chein-I Chang,et al.  Multispectral and hyperspectral image analysis with convex cones , 1999, IEEE Trans. Geosci. Remote. Sens..

[17]  M. Grana,et al.  Further results on AMM for endmember induction , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[18]  Chein-I Chang,et al.  A New Growing Method for Simplex-Based Endmember Extraction Algorithm , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[19]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Hairong Qi,et al.  Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Gregory Asner,et al.  Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..

[22]  Antonio J. Plaza,et al.  A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Gerhard X. Ritter,et al.  Autonomous single-pass endmember approximation using lattice auto-associative memories , 2009, Neurocomputing.

[24]  Antonio J. Plaza,et al.  Parallel implementation of endmember extraction algorithms from hyperspectral data , 2006, IEEE Geoscience and Remote Sensing Letters.

[25]  Mario Winter,et al.  N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.

[26]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[27]  Andreas T. Ernst,et al.  ICE: a statistical approach to identifying endmembers in hyperspectral images , 2004, IEEE Transactions on Geoscience and Remote Sensing.