Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity

2017 ◽  
Vol 11 (04) ◽  
pp. 1 ◽  
Author(s):  
Ying Cui ◽  
Guojiao Song ◽  
Xueting Wang ◽  
Zhongjun Lu ◽  
Liguo Wang
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.


2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  

2021 ◽  
Vol 210 ◽  
pp. 104253
Author(s):  
José F.Q. Pereira ◽  
Maria Fernanda Pimentel ◽  
Ricardo S. Honorato ◽  
Rasmus Bro

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  

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