A Study of Unsupervised Classification Techniques for Hyperspectral Datasets

This work extensively studies and analyses several unsupervised clustering methods for hyperspectral data. We look at unsupervised classification solutions that accomplish adaptive cluster formation in anticipation for new data discoveries. We provide qualitative and quantitative answers to signific...

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Bibliographic Details
Published inIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium pp. 2993 - 2996
Main Authors Yadav, Himanshi, Candela, Alberto, Wettergreen, David
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
classification
Clustering algorithms
Clustering methods
diffusion
Dimensionality reduction
hyperspectral
Hyperspectral imaging
Imaging
learning
Principal component analysis
Unsupervised
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Summary:This work extensively studies and analyses several unsupervised clustering methods for hyperspectral data. We look at unsupervised classification solutions that accomplish adaptive cluster formation in anticipation for new data discoveries. We provide qualitative and quantitative answers to significant problems like high-dimensionality of hyperspectral datasets, multiple sources and relative amounts of existing noise in data and low class separability. The effectiveness of various clustering techniques is illustrated on diverse hyperspectral datasets by intensive experimentation, comparison between techniques and analysis.
ISSN:2153-7003
DOI:10.1109/IGARSS.2019.8900501