Multiscale Principle of Relevant Information for Hyperspectral Image Classification

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 four benchmark data sets demonstrate that MPRI outperforms existing state-of-the-art HSI classification methods (including deep learning based ones) qualitatively and quantitatively, especially in the scenario of limited training samples. I. INTRODUCTION With the rapid development of hyperspectral imaging techniques, current sensors always have high spectral and spatial resolution [1]. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61502195), and in part by the Office of Naval Research Science of Autonomy (Grant No. N000141812306). Yantao Wei is with School of Educational Information Technology, Central China Normal University, Wuhan 430079, China (email: yantaowei@mail.ccnu.edu.cn).