Implications of spectral and spatial features to improve the identification of specific classes

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dc.contributor.authorKallepalli, Akhil
dc.contributor.authorKumar, Anil
dc.contributor.authorKhoshelham, Kourosh
dc.contributor.authorJames, David B.
dc.contributor.authorRichardson, Mark A.
dc.date.accessioned2019-07-08T15:22:06Z
dc.date.available2019-07-08T15:22:06Z
dc.date.issued2019-01-14
dc.description.abstractDimensionality is one of the greatest challenges when deciphering hyperspectral imaging data. Although the multiband nature of the data is beneficial, algorithms are faced with a high computational load and statistical incompatibility due to the insufficient number of training samples. This is a hurdle to downstream applications. The combination of dimensionality and the real-world scenario of mixed pixels makes the identification and classification of imaging data challenging. Here, we address the complications of dimensionality using specific spectral indices from band combinations and spatial indices from texture measures for classification to better identify the classes. We classified spectral and combined spatial–spectral data and calculated measures of accuracy and entropy. A reduction in entropy and an overall accuracy of 80.50% was achieved when using the spectral–spatial input, compared with 65% for the spectral indices alone and 59.50% for the optimally determined principal components.en_UK
dc.identifier.citationKallepalli A, Kumar A, Khoshelham K, et al., Implications of spectral and spatial features to improve the identification of specific classes. Journal of Applied Remote Sensing, Volume 13, Issue 1, 2019, Article number 016504en_UK
dc.identifier.cris22539440
dc.identifier.issn1931-3195
dc.identifier.urihttps://doi.org/10.1117/1.JRS.13.016504
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/14312
dc.language.isoenen_UK
dc.publisherSPIEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleImplications of spectral and spatial features to improve the identification of specific classesen_UK
dc.typeArticleen_UK

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