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Open AccessArticle

Unsupervised Clustering for Hyperspectral Images

The Machine Intelligence and Information Visualization Lab (MintViz), Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for fabrication and control (MANSiD) Research Center, Stefan cel Mare University, Faculty of Electrical Engineering and Computer Science, No. 13, Str. Universitatii, 720229 Suceava, Romania
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Authors to whom correspondence should be addressed.
Current address: “Stefan cel Mare” University, Faculty of Electrical Engineering and Computer Science, No. 13, Str. Universitatii, 720229 Suceava, Romania
Symmetry 2020, 12(2), 277; https://doi.org/10.3390/sym12020277 (registering DOI)
Received: 17 January 2020 / Revised: 4 February 2020 / Accepted: 7 February 2020 / Published: 12 February 2020
Hyperspectral images are becoming a valuable tool much used in agriculture, mineralogy, and so on. The challenge is to successfully classify the materials founded in the field relevant for different applications. Due to a large amount of data corresponding to a big number of spectral bands, the classification programs require a long time to analyze and classify the data. The purpose is to find a better method for reducing the classification time. We exploit various algorithms on real hyperspectral data sets to find out which algorithm is more effective. This paper presents a comparison of unsupervised hyperspectral image classification such as K-means, Hierarchical clustering, and Parafac decomposition, which allows the performance of the model reduction and feature extraction. The results showed that the method useful for big data is the classification of data after Parafac Decomposition.
Keywords: tensor decomposition; parafac decomposition; hyperspectral images; block term decomposition; k-means; hierarchical clustering tensor decomposition; parafac decomposition; hyperspectral images; block term decomposition; k-means; hierarchical clustering
MDPI and ACS Style

Bianca, B.L.; Gheorghe, P.S. Unsupervised Clustering for Hyperspectral Images. Symmetry 2020, 12, 277.

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