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dc.contributor.authorYadav P.P.
dc.contributor.authorShetty A.
dc.contributor.authorRaghavendra B.S.
dc.contributor.authorNarasimhadhan A.V.
dc.identifier.citation2020 IEEE 17th India Council International Conference, INDICON 2020 , Vol. , , p. -en_US
dc.description.abstractHyperspectral imaging is one of the most information-rich sources of remote sensing data that exists. It can capture the entire, continuous spectrum of color and light. Feature extraction techniques that are selected for identifying diagnostic features influence classification accuracy. Spectral matching algorithms like similarity measures are developed to compare spectral features of materials with their reference spec-tral signatures in identifying different earth surfaces. Similarity measures are used as simple feature extraction techniques in target identification using hyperspectral data. Though there are several similarity measures, selecting a robust similarity measure requires further investigation. Influence of similarity measures are not studied much in distinguishing spectrally distinct signatures. In this article, we propose to study the performance of similarity measures in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering signatures of different classes and (iv) endmember extraction. Experimental results show an effective and a robust performance of proposed phase correlation similarity measure among all other similarity measures compared for all the problems under investigation. © 2020 IEEE.en_US
dc.titleEffectiveness of Phase Correlation Spectral Similarity Measure in Distinguishing Target Signatures for Hyperspectral Data Analysisen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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