Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imaging

Simple item page
dc.contributor.authorQiao, Tong
dc.contributor.authorYang, Zhijing
dc.contributor.authorRen, Jinchang
dc.contributor.authorYuen, Peter W. T.
dc.contributor.authorZhao, Huimin
dc.contributor.authorSun, Genyun
dc.contributor.authorMarshall, Stephen
dc.contributor.authorBeneditksson, Jon Atli
dc.date.accessioned2017-12-21T13:21:03Z
dc.date.available2017-12-21T13:21:03Z
dc.date.issued2017-10-10
dc.description.abstractClassification of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for training. It is found that hyperspectral image classification can be generally improved only if the feature extraction technique and the classifier are both addressed. In this paper, a novel classification framework for hyperspectral images based on the joint bilateral filter and sparse representation classification (SRC) is proposed. By employing the first principal component as the guidance image for the joint bilateral filter, spatial features can be extracted with minimum edge blurring thus improving the quality of the band-to-band images. For this reason, the performance of the joint bilateral filter has shown better than that of the conventional bilateral filter in this work. In addition, the spectral similarity-based joint SRC (SS-JSRC) is proposed to overcome the weakness of the traditional JSRC method. By combining the joint bilateral filtering and SS-JSRC together, the superiority of the proposed classification framework is demonstrated with respect to several state-of-the-art spectral-spatial classification approaches commonly employed in the HSI community, with better classification accuracy and Kappa coefficient achieved.en_UK
dc.identifier.citationQiao T, Yang Z, Ren J, et al., Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imaging. Pattern Recognition, Volume 77, May 2018, pp. 316-328en_UK
dc.identifier.cris19078544
dc.identifier.issn0031-3203
dc.identifier.urihttps://doi.org/10.1016/j.patcog.2017.10.008
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/12820
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHyperspectral imagingen_UK
dc.titleJoint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imagingen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Joint_Bilateral_filtering-2017.pdf
Size:
1.78 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (CDS)