Hyper spectral dimensionality reduction using hybrid discriminative local metric learning

2019 ◽  
Vol 71 ◽  
pp. 102904 ◽  
Author(s):  
R. Venkatesan ◽  
S. Prabu
Keyword(s):  
Dimensionality Reduction ◽  
Metric Learning ◽  
Hyper Spectral ◽  
Spectral Dimensionality

Automatic configuration of spectral dimensionality reduction methods

2010 ◽  
Vol 31 (12) ◽  
pp. 1720-1727 ◽  
Author(s):  
Michał Lewandowski ◽  
Dimitrios Makris ◽  
Jean-Christophe Nebel
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Methods ◽  
Spectral Dimensionality

scholarly journals Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772096846
Author(s):  
Jing Liu ◽  
Yulong Qiao
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Image Classification ◽  
Hyperspectral Imaging ◽  
Mahalanobis Distance ◽  
Hyperspectral Image ◽  
Supervised Machine Learning ◽  
Practical Applications ◽  
Spectral Dimensionality

Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. Dimensionality reduction has a strong influence on image classification performance with the problems of strong coupling features and high band correlation. To solve these issues, we propose the Mahalanobis distance–based kernel supervised machine learning framework for spectral dimensionality reduction. With Mahalanobis distance matrix–based dimensional reduction, the coupling relationship between features and the elimination of the scale effect are removed in low-dimensional feature space, which benefits the image classification. The experimental results show that compared with other methods, the proposed algorithm demonstrates the best accuracy and efficiency. The Mahalanobis distance–based multiples kernel learning achieves higher classification accuracy than the Euclidean distance kernel function. Accordingly, the proposed Mahalanobis distance–based kernel supervised machine learning method performs well with respect to the spectral dimensionality reduction in hyperspectral imaging remote sensing.

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