Spectral–Spatial Fractal Residual Convolutional Neural Network With Data Balance Augmentation for Hyperspectral Classification

Abstract

The development of deep learning has brought new prospects into the field of hyperspectral classification, and the classification ability of this method for the classification of hyperspectral images (HSIs) has been continuously improved. However, there are still some problems that must be solved; for example, the spectral–spatial features of HSIs are not effectively extracted, the labeled samples in the data set are limited, and the number of samples in different categories is imbalanced. To facilitate the progress of hyperspectral classification, a spectral–spatial fractal residual convolutional neural network with data balance augmentation is proposed here. In this network, a data balance augmentation approach that can solve the problems of limited labeled data and imbalanced categories is proposed. In addition, the spectral–spatial residual module is proposed to learn the spectral–spatial information and alleviate the problem of model degradation effectively. In addition, the spectral–spatial focal structure, which can guarantee the integrity of the information, is introduced. Moreover, the spectral–spatial dimensional transformation module, which can reduce the size and number of hyperspectral feature maps without losing the fine features, is presented. In particular, the proposed network has a strong ability to classify the categories that have a small number of samples and reaches the state-of-the-art level for three benchmark data sets.