  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}.
