4.5 Article

Unsupervised Clustering for Hyperspectral Images

Journal

SYMMETRY-BASEL
Volume 12, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/sym12020277

Keywords

tensor decomposition; parafac decomposition; hyperspectral images; block term decomposition; k-means; hierarchical clustering

Funding

  1. European Social Fund [36355/23.05.2019 HRD OP/380/6/13-SMIS, 123847]

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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.

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