Multidimensional feature extraction from 3D hyperspectral images

A hyperspectral imaging system has been set up and used to capture hyperspectral image cubes from various samples in the 400-1000 nm spectral region. The system consists of an imaging spectrometer attached to a CCD camera with fiber optic light source as the illuminator. The significance of this system lies in its capability to capture 3D spectral and spatial data that can then be analyzed to extract information about the underlying samples, monitor the variations in their response to perturbation or changing environmental conditions, and compare optical properties. In this paper preliminary results are presented that analyze the 3D spatial and spectral data in reflection mode to extract features to differentiate among different classes of interest using biological and metallic samples. Studied biological samples possess homogenous as well as non-homogenous properties. Metals are analyzed for their response to different surface treatments, including polishing. Similarities and differences in the feature extraction process and results are presented. The mathematical approach taken is discussed. The hyperspectral imaging system offers a unique imaging modality that captures both spatial and spectral information that can then be correlated for future sample predictions.

[1]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[2]  Anthony A. Maciejewski,et al.  An example of principal component analysis applied to correlated images , 2001, Proceedings of the 33rd Southeastern Symposium on System Theory (Cat. No.01EX460).

[3]  Vikram Jayaram,et al.  Active learning schemes for reduced dimensionality hyperspectral classification , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[4]  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.

[5]  Shaohui Mei,et al.  Spectral-spatial Endmember Extraction by Singular Value Decomposition for AVIRIS data , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[6]  Gabriele Moser,et al.  Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Sean Danaher,et al.  Singular value decomposition in applied remote sensing , 1996 .

[8]  James F. Scholl,et al.  Hyperspectral feature classification with alternate wavelet transform representations , 2006, SPIE Optics + Photonics.

[9]  Yang Tao,et al.  Integrated PCA-FLD method for hyperspectral imagery feature extraction and band selection , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[10]  Russell M. Mersereau,et al.  On the impact of PCA dimension reduction for hyperspectral detection of difficult targets , 2005, IEEE Geoscience and Remote Sensing Letters.

[11]  Licheng Jiao,et al.  Feature Extraction Combining PCA and Immune Clonal Selection for Hyperspectral Remote Sensing Image Classification , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[12]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[13]  David W. Opitz,et al.  AN INTEGRATED APPROACH TO HYPERSPECTRAL FEATURE EXTRACTION , 2008 .

[14]  F. Del Frate,et al.  A COMPARISON OF FEATURE EXTRACTION METHODOLOGIES APPLIED ON HYPERSPECTRAL DATA , 2010 .

[15]  Michael J. Gaffey,et al.  Reflectance spectra of iron meteorites: Implications for spectral identification of their parent bodies , 2010 .

[16]  Robert P. W. Duin,et al.  Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Nicolas H. Younan,et al.  Hyperspectral Pixel Unmixing via Spectral Band Selection and DC-Insensitive Singular Value Decomposition , 2007, IEEE Geoscience and Remote Sensing Letters.

[18]  M. Mendenhall,et al.  Relevance-Based Feature Extraction for Hyperspectral Images , 2008, IEEE Transactions on Neural Networks.