Superpixel-based 3D deep neural networks for hyperspectral image classification

Abstract

This paper presents a novel hyperspectral image (HSI) classification method to effectively exploit the 3D spectral-spatial information via superpixel-based 3D deep neural networks (3D DNNs). Superpixel can represent the structure of HSI with adaptive sizes and shapes, and therefore, it is incorporated into 3D DNNs to improve the classification performance, especially for noisy classification and boundary misclassification. First, a spatial feature image via superpixel is constructed to increase the spectral-spatial similarity and diversity. Second, a 3D superpixel-based sample filling method is designed to solve the misclassification problem of boundaries. Third, a 3D recurrent convolutional networks (3D RCNNs) are designed to further exploit spatial continuity and suppress noisy prediction. Experimental results on real HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both visual appearance and classification accuracy.