Hyperspectral Image Clustering: Current Achievements and Future Lines

被引:75
作者
Zhai, Han [1 ]
Zhang, Hongyan [2 ]
Li, Pingxiang [2 ]
Zhang, Liangpei [2 ]
机构
[1] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
基金
中国国家自然科学基金;
关键词
Hyperspectral imaging; Clustering methods; Clustering algorithms; Data models; Computational modeling; Biological system modeling; Task analysis; FUZZY C-MEANS; UNSUPERVISED CLASSIFICATION; MIXTURE MODEL; DIFFERENTIAL EVOLUTION; SPATIAL INFORMATION; MEAN-SHIFT; ALGORITHM; SEGMENTATION; EXTRACTION; REDUCTION;
D O I
10.1109/MGRS.2020.3032575
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Hyperspectral remote sensing organically combines traditional space imaging with advanced spectral measurement technologies, delivering advantages stemming from continuous spectrum data and rich spatial information. This development of hyperspectral technology takes remote sensing into a brand-new phase, making the technology widely applicable in various fields. Hyperspectral clustering analysis is widely utilized in hyperspectral image (HSI) interpretation and information extraction, which can reveal the natural partition pattern of pixels in an unsupervised way. In this article, current hyperspectral clustering algorithms are systematically reviewed and summarized in nine main categories: centroid-based, density-based, probability-based, bionics-based, intelligent computing-based, graph-based, subspace clustering, deep learning-based, and hybrid mechanism-based. The performance of several popular hyperspectral clustering methods is demonstrated on two widely used data sets. HSI clustering challenges and possible future research lines are identified. © 2013 IEEE.
引用
收藏
页码:35 / 67
页数:33
相关论文
共 183 条
[1]  
Acito N, 2003, INT GEOSCI REMOTE SE, P3745
[2]   Delineation of soil variability using geostatistics and fuzzy clustering analyses of hyperspectral data [J].
Ahn, CW ;
Baumgardner, MF ;
Biehl, LL .
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 1999, 63 (01) :142-150
[3]  
Alsabti S., 1997, Electr. Eng. Comput. Sci., V43
[4]  
[Anonymous], 2015, DEEP LEARNING NONPAR
[5]  
[Anonymous], 2010, P NIPS WORKSH CHALL
[6]  
[Anonymous], 2001, P WORKSH PERC ORG CO
[7]  
[Anonymous], framework modelled with fully and semi-supervised reciprocal learning
[8]   UNSUPERVISED CLASSIFICATION OF REMOTELY SENSED IMAGES USING GAUSSIAN MIXTURE MODELS AND PARTICLE SWARM OPTIMIZATION [J].
Ari, Caglar ;
Aksoy, Selim .
2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, :1859-1862
[9]  
Bacca J., 2017, P MATH IM OPT SOC AM
[10]  
Backlund H., 2011, Data Min. TNM033, P11