Fast Implementation of Maximum Simplex Volume-Based Endmember Extraction in Original Hyperspectral Data Space

Endmember extraction (EE) is a prerequisite task for spectral analysis of hyperspectral imagery. In all kinds of EE algorithms, maximum simplex volume-based ones, such as simplex growing algorithm (SGA) and N-FINDR algorithm, have been widely used for their fully automated and efficient performance. However, implementation of the algorithms needs dimension reduction of original data, and the algorithms include innumerable volume calculation. This leads to a low speed of the algorithms and thus becomes a limitation to their applications. In this paper, a simple distance measure is presented, and then, fast SGA and fast N-FINDR algorithm are constructed based on a proposed distance measure, which is free of dimension reduction and makes use of distance measure instead of volume evaluation to speed up the algorithm. The complexity of the proposed methods is compared with the original algorithms by theoretical analysis. Experiments show that the implementation of the two improved EE algorithms is much faster than that of the two original maximum simplex volume-based EE algorithms.

[1]  Chein-I Chang,et al.  Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery , 2011, IEEE Transactions on Image Processing.

[2]  Chein-I Chang,et al.  Real-Time Simplex Growing Algorithms for Hyperspectral Endmember Extraction , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[3]  A. Chowdhury,et al.  Fast implementation of N-FINDR algorithm for endmember determination in hyperspectral imagery , 2007, SPIE Defense + Commercial Sensing.

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

[5]  Alfonso Fernández-Manso,et al.  Spectral unmixing , 2012 .

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

[7]  Konstantinos Kalpakis,et al.  Fast Algorithms to Implement N-FINDR for Hyperspectral Endmember Extraction , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[9]  Antonio J. Plaza,et al.  Parallel implementation of the N-FINDR endmember extraction algorithm on commodity graphics processing units , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

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

[12]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

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

[14]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Hao Zhang,et al.  Synchronous, asynchronous and grouping asynchronous parallel implementation for N-FINDR algorithms in hyperspectral remote sensing image , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[16]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[17]  Chein-I Chang,et al.  Improved Process for Use of a Simplex Growing Algorithm for Endmember Extraction , 2009, IEEE Geoscience and Remote Sensing Letters.

[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]  Chein-I. Chang,et al.  An improved N-FINDR algorithm in implementation , 2005 .

[21]  Chein-I Chang,et al.  Statistics-based endmember extraction algorithms for hyperspectral imagery , 2007, SPIE Organic Photonics + Electronics.

[22]  Danfeng Liu,et al.  Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery , 2012 .

[23]  Chein-I Chang,et al.  Sequential N-FINDR algorithms , 2008, Optical Engineering + Applications.

[24]  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.

[25]  Antonio J. Plaza,et al.  Recent Developments in High Performance Computing for Remote Sensing: A Review , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Lei Guo,et al.  Using a New Search Strategy to Improve the Performance of N-FINDR Algorithm for End-Member Determination , 2009, 2009 2nd International Congress on Image and Signal Processing.

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

[28]  Chein-I Chang,et al.  Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[29]  Liguo Wang,et al.  Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images , 2013, IEEE Geosci. Remote. Sens. Lett..

[30]  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.