18 May 2013 Advanced spectral signature discrimination algorithm
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This paper presents a novel approach to the task of hyperspectral signature analysis. Hyperspectral signature analysis has been studied a lot in literature and there has been a lot of different algorithms developed which endeavors to discriminate between hyperspectral signatures. There are many approaches for performing the task of hyperspectral signature analysis. Binary coding approaches like SPAM and SFBC use basic statistical thresholding operations to binarize a signature which are then compared using Hamming distance. This framework has been extended to techniques like SDFC wherein a set of primate structures are used to characterize local variations in a signature together with the overall statistical measures like mean. As we see such structures harness only local variations and do not exploit any covariation of spectrally distinct parts of the signature. The approach of this research is to harvest such information by the use of a technique similar to circular convolution. In the approach we consider the signature as cyclic by appending the two ends of it. We then create two copies of the spectral signature. These three signatures can be placed next to each other like the rotating discs of a combination lock. We then find local structures at different circular shifts between the three cyclic spectral signatures. Texture features like in SDFC can be used to study the local structural variation for each circular shift. We can then create different measure by creating histogram from the shifts and thereafter using different techniques for information extraction from the histograms. Depending on the technique used different variant of the proposed algorithm are obtained. Experiments using the proposed technique show the viability of the proposed methods and their performances as compared to current binary signature coding techniques.
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Sumit Chakravarty, Sumit Chakravarty, Wenjie Cao, Wenjie Cao, Alim Samat, Alim Samat, } "Advanced spectral signature discrimination algorithm", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431J (18 May 2013); doi: 10.1117/12.2011284; https://doi.org/10.1117/12.2011284


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