R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

Deep-learning-based methods have displayed promising performance for hyperspectral image (HSI) classification, due to their capacity of extracting deep features from HSI. However, these methods usually require a large number of training samples. It is quite difficult for deep-learning model to provide representative feature expression for HSI data when the number of samples are limited. In this paper, a novel simplified deep-learning model, rolling guidance filter (RGF) and vertex component analysis network (R-VCANet), is proposed, which achieves higher accuracy when the number of training samples is not abundant. In R-VCANet, the inherent properties of HSI data, spatial information and spectral characteristics, are utilized to construct the network. And by this means the obtained model could generate more powerful feature expression with less samples. First, spectral and spatial information are combined via the RGF, which could explore the contextual structure features and remove small details from HSI. More importantly, we have designed a new network called vertex component analysis network for deep features extraction from the smoothed HSI. Experiments on three popular datasets indicate that the proposed R-VCANet based method reveals better performance than some state-of-the-art methods, especially when the training samples available are not abundant.