Spatial spectral band selection for enhanced hyperspectral remote sensing classification applications

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dc.contributor.authorMoya Torres, Ruben
dc.contributor.authorYuen, Peter W. T.
dc.contributor.authorYuan Changfeng
dc.contributor.authorPiper, Jonathan
dc.contributor.authorMcCullough, Chris
dc.contributor.authorGodfree Peter
dc.date.accessioned2020-11-19T17:23:58Z
dc.date.available2020-11-19T17:23:58Z
dc.date.issued2020-08-31
dc.date.updated2020-11-19T17:02:41Z
dc.description.abstractDespite the numerous band selection (BS) algorithms reported in the field, most if not all have exhibited maximal accuracy when more spectral bands are utilized for classification. This apparently disagrees with the theoretical model of the ‘curse of dimensionality’ phenomenon, without apparent explanations. If it were true, then BS would be deemed as an academic piece of research without real benefits to practical applications. This paper presents a spatial spectral mutual information (SSMI) BS scheme that utilizes a spatial feature extraction technique as a preprocessing step, followed by the clustering of the mutual information (MI) of spectral bands for enhancing the efficiency of the BS. Through the SSMI BS scheme, a sharp ’bell’-shaped accuracy-dimensionality characteristic that peaks at about 20 bands has been observed for the very first time. The performance of the proposed SSMI BS scheme has been validated through 6 hyperspectral imaging (HSI) datasets (Indian Pines, Botswana, Barrax, Pavia University, Salinas, and Kennedy Space Center (KSC)), and its classification accuracy is shown to be approximately 10% better than seven state-of-the-art BS schemes (Saliency, HyperBS, SLN, OCF, FDPC, ISSC, and Convolution Neural Network (CNN)). The present result confirms that the high efficiency of the BS scheme is essentially important to observe and validate the Hughes’ phenomenon in the analysis of HSI data. Experiments also show that the classification accuracy can be affected by as much as approximately 10% when a single ‘crucial’ band is included or missed out for classification.en_UK
dc.identifier10.3390/jimaging6090087
dc.identifier.citationMoya Torres R, Yuen PW, Yuan C, et al., (2020) Spatial spectral band selection for enhanced hyperspectral remote sensing classification applications. Journal of Imaging, Volume 6, Issue 9, August 2020, Article number 87en_UK
dc.identifier.issn2313-433X
dc.identifier.urihttps://doi.org/10.3390/jimaging6090087
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16017
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectband selectionen_UK
dc.subjectspatial spectral band selectionen_UK
dc.subjecthyperspectral imagingen_UK
dc.subjectclassificationen_UK
dc.subjectmutual informationen_UK
dc.subjectcurse of dimensionalityen_UK
dc.subjectHughes phenomenonen_UK
dc.subjectaccuracy-dimensionality characteristicsen_UK
dc.titleSpatial spectral band selection for enhanced hyperspectral remote sensing classification applicationsen_UK
dc.typeArticleen_UK

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