# A novel statistical metric learning for hyperspectral image classification

Source: [A novel statistical metric learning for hyperspectral image classification](<https://www.sota2.com/research/paper/68fa65bc1e209131cea0bf9c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:33:37.375Z

Authors: Zhiqiang Gong, Ping Zhong, Weidong Hu, Zixuan Xiao, Xuping Yin

Published: 2019-05-13T15:28:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.05087>)

[PDF](<https://arxiv.org/pdf/1905.05087.pdf>)