Finding endmember classes in hyperspectral imagery

Endmember finding has received considerable interest in hyperspectral imaging. In reality an endmember finding algorithm (EFA) suffers from endmember variability which causes inaccuracy, inconsistency and instability. In this case a real endmember may not exist but rather appears as its variant, referred to as virtual signature (VS). This paper presents a new approach to finding VSs by taking endmember variability into account. It first determines a required number of endmember classes by virtual dimensionality (VD), then designs an unsupervised method to find endmember classes and finally develops an iterative algorithm to find VSs. Comprehensive experiments including synthetic and real image scenes are conducted to demonstrate effectiveness of the proposed approach.

[1]  D. Roberts,et al.  Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE , 2003 .

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

[3]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[4]  Antonio J. Plaza,et al.  Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Chein-I Chang,et al.  Automatic spectral target recognition in hyperspectral imagery , 2003 .

[6]  Pol Coppin,et al.  Endmember variability in Spectral Mixture Analysis: A review , 2011 .

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

[8]  Conghe Song,et al.  Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability? , 2005 .

[9]  Chein-I Chang,et al.  Endmember variability resolved by pixel purity index in hyperspectral imagery , 2014, Sensing Technologies + Applications.

[10]  Chein-I Chang,et al.  Unsupervised fully constrained squares linear spectral mixture analysis method for multispectral imagery , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[11]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[12]  Wei Xiong,et al.  Maximum Orthogonal Subspace Projection Approach to Estimating the Number of Spectral Signal Sources in Hyperspectral Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.

[13]  Jing Jin,et al.  A Novel Approach Based on Fisher Discriminant Null Space for Decomposition of Mixed Pixels in Hyperspectral Imagery , 2010, IEEE Geoscience and Remote Sensing Letters.

[14]  Wei Xiong,et al.  A Theory of High-Order Statistics-Based Virtual Dimensionality for Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  K. C. Ho,et al.  Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing , 2014, IEEE Signal Processing Magazine.

[16]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[17]  Chein-I Chang,et al.  Finding Endmembers in Hyperspectral Imagery , 2013 .

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

[19]  Chein-I Chang,et al.  Hyperspectral Data Processing: Algorithm Design and Analysis , 2013 .

[20]  Benoit Rivard,et al.  Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery , 2006 .