Semisupervised classification of hyperspectral images with low-rank representation kernel

2020 ◽  
Vol 37 (4) ◽  
pp. 606 ◽  
Author(s):  
Seyyed Ali Ahmadi ◽  
Nasser Mehrshad
Keyword(s):  
Hyperspectral Images ◽  
Low Rank ◽  
Semisupervised Classification ◽  
Low Rank Representation

Local Hypergraph Laplacian Regularized Low-Rank Representation for Noise Reduction of Hyperspectral Images

2017 ◽  
Vol 37 (5) ◽  
pp. 0510001 ◽  
Author(s):  
薛志祥 Xue Zhixiang ◽  
余旭初 Yu Xuchu ◽  
谭 熊 Tan Xiong ◽  
付琼莹 Fu Qiongying
Keyword(s):  
Noise Reduction ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Low Rank Representation ◽  
Hypergraph Laplacian

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

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.

Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

2016 ◽  
Vol 54 (6) ◽  
pp. 3410-3420 ◽  
Author(s):  
Frank de Morsier ◽  
Maurice Borgeaud ◽  
Volker Gass ◽  
Jean-Philippe Thiran ◽  
Devis Tuia
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Sparse Graph

Simultaneous Spatial and Spectral Low-Rank Representation of Hyperspectral Images for Classification

2018 ◽  
Vol 56 (5) ◽  
pp. 2872-2886 ◽  
Author(s):  
Shaohui Mei ◽  
Junhui Hou ◽  
Jie Chen ◽  
Lap-Pui Chau ◽  
Qian Du
Keyword(s):  
Hyperspectral Images ◽  
Low Rank ◽  
Low Rank Representation

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
Keyword(s):  
Hyperspectral Images ◽  
Training Algorithm ◽  
Semisupervised Classification
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