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
收藏
页数:13
相关论文
共 26 条
[1]   Tensor decompositions for feature extraction and classification of high dimensional datasets [J].
Anh Huy Phan ;
Ciehoeki, Andrzej .
IEICE NONLINEAR THEORY AND ITS APPLICATIONS, 2010, 1 (01) :37-68
[2]   The CNN paradigm: Shapes and complexity [J].
Arena, P ;
Bucolo, M ;
Fazzino, S ;
Fortuna, L ;
Frasca, M .
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2005, 15 (07) :2063-2090
[3]   Deep Learning for Classification of Hyperspectral Data [J].
Audebert, Nicolas ;
Le Saux, Bertrand ;
Lefevre, Sebastien .
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2019, 7 (02) :159-173
[4]   Analysis of Hyperspectral Images Using Supervised Learning Techniques [J].
Bilius, Laura-Bianca ;
Pentiuc, Stefan-Gheorghe ;
Brie, David ;
Miron, Sebastian .
2019 23RD INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC), 2019, :675-680
[5]  
Christian E., 2019, P 16 INT S I SPAN 20, DOI [10.1007/978-3-030-30143-9, DOI 10.1007/978-3-030-30143-9]
[6]   Predicting run time of classification algorithms using meta-learning [J].
Doan, Tri ;
Kalita, Jugal .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2017, 8 (06) :1929-1943
[7]  
Domanov I, 2013, SIAM J MATRIX ANAL A, V34, P876, DOI 10.1137/120877258
[8]  
El Rahman S A., 2016, International Journal of Advanced Computer Science and Applications(IJACSA), V7, DOI DOI 10.14569/IJACSA.2016.070425
[9]   The HOTSAT volcano monitoring system based on combined use of SEVIRI and MODIS multispectral data [J].
Ganci, Gaetana ;
Vicari, Annamaria ;
Fortuna, Luigi ;
Del Negro, Ciro .
ANNALS OF GEOPHYSICS, 2011, 54 (05) :544-550
[10]  
GeeksforGeeks, COMP SCI PORT GEEKS