Attention-Based Deep Convolutional Capsule Network for Hyperspectral Image Classification

Hyperspectral remote sensing image analysis employing deep learning (DL) models has consistently demonstrated remarkable performance, owing to their robust nonlinear modeling and end-to-end optimization capabilities. Notably, the capsule neural network (CapsNet) has attracted substantial attention for its proficient feature extraction capabilities. However, it tends to overlook the inherent spatial heterogeneity within patch features. In this paper, we introduce a spatial attention-based deep convolutional capsule network (SA-CapsNet) to enhance CapsNet’s performance in hyperspectral image (HSI) classification. The incorporation of a more potent and light-weight spatial attention mechanism introduces diversity among neighboring pixels. Additionally, we enhance the stability of learned spectral-spatial features by implementing a convolutional capsule layer that extends dynamic routing with 3D convolution. Experimental results conducted on three commonly used hyperspectral datasets demonstrate that SA-CapsNet outperforms conventional and state-of-the-art DL-based HSI classification algorithms in terms of classification accuracy and computational efficiency.