Unsupervised Clustering for Hyperspectral Images

被引:8
作者
Bilius, Laura Bianca [1 ,2 ]
Pentiuc, Stefan Gheorghe [1 ,2 ]
机构
[1] Stefan cel Mare Univ, Machine Intelligence & Informat Visualizat Lab Mi, Integrated Ctr Res Dev & Innovat Adv Mat Nanotech, Res Ctr,Fac Elect Engn & Comp Sci, 13 Str Univ, Suceava 720229, Romania
[2] Stefan cel Mare Univ, Fac Elect Engn & Comp Sci, 13 Str Univ, Suceava 720229, Romania
来源
SYMMETRY-BASEL | 2020年 / 12卷 / 02期
关键词
tensor decomposition; parafac decomposition; hyperspectral images; block term decomposition; k-means; hierarchical clustering; CANONICAL POLYADIC DECOMPOSITION; CLASSIFICATION; ALGORITHMS;
D O I
10.3390/sym12020277
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:13
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