# Multiscale Principle of Relevant Information for Hyperspectral Image Classification

Source: [Multiscale Principle of Relevant Information for Hyperspectral Image Classification](<https://www.sota2.com/research/paper/68fa6a211e209131cea262af>)

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

Data updated: 2025-10-25T23:16:37.291Z

Authors: Yantao Wei, Shujian Yu, Luis Sanchez Giraldo, Jose C. Principe

Published: 2019-07-13T07:29:42.000Z

## Abstract

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 \\url{http://bit.ly/MPRI\_HSI}.

## 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/1907.06022>)

[PDF](<https://arxiv.org/pdf/1907.06022.pdf>)