Recurrent Feedback Convolutional Neural Network for Hyperspectral Image Classification

Abstract

Deep neural networks have achieved promising performance for hyperspectral image (HSI) classification. However, due to the limitation of the available labeled samples, the traditional deeper and wider neural networks usually cause the overfitting problem and lose the detailed information. To solve this problem, a brain-like structure, namely spatial attention-driven recurrent feedback convolutional neural network (SARFNN), is proposed by utilizing the recurrent feedback and attention mechanism structures, from which two deep models are further developed for HSI classification. First, a 2-D SARFNN (SARF2DNN) model is developed to learn the spatial features from HSI data. After that, to better exploit the 3-D characteristic, the 3-D version is extended from SARF2DNN, thus constructing an SARF3DNN model to extract joint spatial-spectral features. Moreover, with the help of the idea of brain-likeness, the recurrent feedback module is designed to recover information loss caused by deeper structure and the dimension reduction operation. The experimental results conducted on two HSI data sets show that our SARFNN architecture can achieve more competitive performance than other state-of-the-art algorithms.