Iterative Random Training Sampling Spectral Spatial Classification for Hyperspectral Images

Simple item page
dc.contributor.authorChang, Chein-I
dc.contributor.authorMa, Kenneth Yeonkong
dc.contributor.authorLiang, Chia-Chen
dc.contributor.authorKuo, Yi-Mei
dc.contributor.authorChen, Shuhan
dc.contributor.authorZhong, Shengwei
dc.date.accessioned2022-11-09T18:03:26Z
dc.date.available2022-11-09T18:03:26Z
dc.date.issued2020-07-09
dc.description.abstractHyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training data. This is because a different set of training samples may produce a different classification result. A general approach to addressing this problem is the so-called K-fold method which implements RTS K times and takes the average of overall accuracy with respect to standard deviation to describe a confidence level of classification performance. To deal with this issue, this article develops an iterative RTS (IRTS) method as an alternative to the K-fold method to reduce the uncertainty caused by RTS. Its idea is to add the spatial filtered classification maps to the image cube that is currently being processed via feedback loops to augment image cubes iteratively. Then, the training samples will be reselected randomly from the new augmented image cubes iteration-by-iteration. As a result, the training samples selected from each iteration will be updated by new added spatial information captured by spatial filters implemented at the iteration. The experimental results clearly demonstrate that IRTS successfully improves classification accuracy as well as reduces inconsistency in results.en_US
dc.description.sponsorshipThe work of Chein-I Chang was supported by the Fundamental Research Funds for Central Universities under Grant 3132019341.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9137652en_US
dc.format.extent22 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2ywpr-yh1i
dc.identifier.citationC. -I. Chang, K. Y. Ma, C. -C. Liang, Y. -M. Kuo, S. Chen and S. Zhong, "Iterative Random Training Sampling Spectral Spatial Classification for Hyperspectral Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3986-4007, 2020, doi: 10.1109/JSTARS.2020.3008359.en_US
dc.identifier.urihttps://doi.org/10.1109/JSTARS.2020.3008359
dc.identifier.urihttp://hdl.handle.net/11603/26287
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleIterative Random Training Sampling Spectral Spatial Classification for Hyperspectral Imagesen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-5450-4891en_US
dcterms.creatorhttps://orcid.org/0000-0003-3636-5311en_US
dcterms.creatorhttps://orcid.org/0000-0001-9996-0666en_US
dcterms.creatorhttps://orcid.org/0000-0001-8317-728Xen_US

Files

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

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection