Spectral dimensionality reduction for Bregman information

2019 ◽  
Vol 2 (2) ◽  
pp. 273-282
Author(s):  
Atsuya Kumagai
Keyword(s):  
Dimensionality Reduction ◽  
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.

scholarly journals Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications

2021 ◽  
Vol 13 (9) ◽  
pp. 1647
Author(s):  
Fraser Macfarlane ◽  
Paul Murray ◽  
Stephen Marshall ◽  
Henry White
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Effective Means ◽  
Reduction Process ◽  
Hyperspectral Images ◽  
Detection Algorithms ◽  
Wide Range ◽  
Reduction Methods ◽  
Spectral Dimensionality ◽  
Spatial Redundancy

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.

Multiple Kernel Learning for Spectral Dimensionality Reduction

2015 ◽  
pp. 626-634 ◽  
Author(s):  
Diego Hernán Peluffo-Ordóñez ◽  
Andrés Eduardo Castro-Ospina ◽  
Juan Carlos Alvarado-Pérez ◽  
Edgardo Javier Revelo-Fuelagán
Keyword(s):  
Dimensionality Reduction ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Multiple Kernel ◽  
Spectral Dimensionality
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