# A novel statistical metric learning for hyperspectral image classification

## TL;DR

This paper introduces a new statistical metric learning method for hyperspectral image classification that reduces intra-class variance and increases inter-class separation, improving classification accuracy.

## Contribution

It proposes a novel spectral-spatial metric learning approach combining intra-class variance reduction and inter-class separation for hyperspectral data.

## Key findings

- Effective on two real-world hyperspectral datasets
- Improves classification accuracy
- Demonstrates the benefit of the proposed metric learning approach

## Abstract

In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image. First, the standard variance of the samples of each class in each batch is used to decrease the intra-class variance within each class. Then, the distances between the means of different classes are used to penalize the inter-class variance of the training samples. Finally, the standard variance between the means of different classes is added as an additional diversity term to repulse different classes from each other. Experiments have conducted over two real-world hyperspectral image datasets and the experimental results have shown the effectiveness of the proposed statistical metric learning.