A Supervised-Learning $p$ -Norm Distance Metric for Hyperspectral Remote Sensing Image Classification

Abstract

Hyperspectral remote sensing images present rich information on the characteristics of different physical materials. Utilizing the rich information, classifiers can distinguish these different materials. The minimum distance technique, which is commonly used in classification, is sensitive to the distance metric, especially in high-dimensional space. In this letter, we study the effect of the $p$ -norm distance metric on the minimum distance technique and propose a supervised-learning $p$ -norm distance metric to optimize the value of $p$ . In the experimental study, we take the minimum distance and the $k$ -nearest neighbor classifiers as examples to test the proposed supervised-learning $p$ -norm distance metric. The results suggest that the supervised-learning $p$ -norm distance metric can improve the performance of a classifier for hyperspectral remote sensing image classification.