Composite Kernels for Support Vector Classification of Hyper-Spectral Data
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Accurate estimation of ICA weight matrix by implicit constraint imposition using lie group
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A spectral approach to nonlocal mesh editing
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
New dissimilarity measures for ultraviolet spectra identification
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
A comparison on the effectiveness of different similarity measures for image classification
Proceedings of the 49th Annual Southeast Regional Conference
Spectral distortion in lossy compression of hyperspectral data
Journal of Electrical and Computer Engineering
Feature identification and extraction in function fields
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Interactive visualization of function fields by range-space segmentation
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Journal of Information Science
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A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity and discrimination. We present a new hyperspectral measure, the spectral information measure (SIM), to describe spectral variability and two criteria, spectral information divergence and spectral discriminatory probability for spectral similarity and discrimination, respectively. The spectral information measure is an information-theoretic measure which treats each pixel as a random variable using its spectral signature histogram as the desired probability distribution. Spectral information divergence (SID) compares the similarity between two pixels by measuring the probabilistic discrepancy between two corresponding spectral signatures. The spectral discriminatory probability calculates spectral probabilities of a spectral database (library) relative to a pixel to be identified so as to achieve material identification. In order to compare the discriminatory power of one spectral measure relative to another, a criterion is also introduced for performance evaluation, which is based on the power of discriminating one pixel from another relative to a reference pixel. The experimental results demonstrate that the new hyperspectral measure can characterize spectral variability more effectively than the commonly used spectral angle mapper (SAM)