Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral-spatial hyperspectral image (HSI) classification. However, conventional spatial-context based CNNs commonly adopt the single patchwise scheme to represent the to-be-classified samples, which often fails to completely investigate the wealthy spectral-spatial information in complicated situations. For instance, it has great probability to cause misclassifications on the irregular or inhomogeneous areas, especially for the borders across different classes. To counteract this deficiency, we propose a unified multi-direction network (MDN) for HSI classification, which can exhaustively explore the abundant spectral and detailed spatial-context information through multi-direction samples. Additionally, considering the image-spectrum merged structure of the HSI, 3D Squeeze-and-Excitation residual (3DSERes) blocks are devised in each stream of the framework to consecutively learn the spectral and spatial from low-level to high-level features. Specifically, the 3DSERes can not only facilitate fluent gradient in backpropagation through skip-connections, but also emphasize the significant spectral-spatial features and constrain the futile ones. This characteristic is beneficial to enhance the model's generalization capability even with limited training samples. Furthermore, for properly aggregating the multi-direction deep features, we exploit the simple yet effective attentional spectral prior (ASP) creatively through leveraging the original spectral correlations. Extensive experiment results on three benchmark datasets indicate that the proposed MDN-ASP can achieve promising classification performance compared to the state-of-the-art methods.