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

Abstract

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.

Editorial

“…Several experiments are given to indicate that the performance of the proposed machine learning and deep learning methods could be better than that of the traditional machine learning methods. [61][62][63] In the future, the semantic web could be considered to represent the sensing data from distributed sensor networks. [64][65][66][67] For improving the performance of machine learning-based distributed sensor network applications, the advanced swarm intelligence techniques [68][69][70] could be applied.…”

“…Several experiments are given to indicate that the performance of the proposed machine learning and deep learning methods could be better than that of the traditional machine learning methods. 61–63…”

“…The practical experimental results show that the performance of the optimization method-based Mahalanobis distance was higher than that of the optimization method based on the Euclidean distance and Kappa coefficient. 62…”