# Multiscale Principle of Relevant Information for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1907.06022 · HTML: https://mlsanity.com/paper/1907.06022/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Self-organization Preserved Graph Structure Learning with Principle of Relevant Information](https://mlsanity.com/paper/2301.00015/) (2022)
- [A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver](https://mlsanity.com/paper/2411.06969/) (2024)

## Abstract (arXiv)

This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI) classification. MPRI inherits the merits of the principle of relevant information (PRI) to effectively extract multiscale information embedded in the given data, and also takes advantage of the multilayer structure to learn representations in a coarse-to-fine manner. Specifically, MPRI performs spectral-spatial pixel characterization (using PRI) and feature dimensionality reduction (using regularized linear discriminant analysis) iteratively and successively. Extensive experiments on three benchmark data sets demonstrate that MPRI outperforms existing state-of-the-art methods (including deep learning based ones) qualitatively and quantitatively, especially in the scenario of limited training samples. Code of MPRI is available at urlhttp://bit.ly/MPRIHSI.