This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image 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 http://bit.ly/MPRI_HSI.

本文提出了一种新的架构，称为多尺度相关信息原理（MPRI），以学习用于高光谱图像分类的判别光谱空间特征。MPRI 继承了相关信息（PRI）原理的优点，可以有效地提取嵌入给定数据中的多尺度信息，并利用多层结构以粗到细的方式学习表示。具体来说，MPRI 迭代地、连续地执行光谱空间像素表征（使用 PRI）和特征降维（使用正则化线性判别分析）。在三个基准数据集上的大量实验表明，MPRI 在定性和定量上都优于现有的最先进方法（包括基于深度学习的方法），特别是在训练样本有限的情况下。MPRI 代码可在 http://bit.ly/MPRI_HSI 获得。