Hybrid Local and Nonlocal 3-D Attentive CNN for Hyperspectral Image Super-Resolution

Abstract

A deep convolutional neural network (CNN) has shown its great potential in hyperspectral image (HSI) super-resolution (SR). Integrating CNN with attention mechanism is expected to boost the SR performance. However, how to learn attention along the spectral, spatial, and channel dimensions of HSI is still an open issue, and the current attention mechanism is not efficient in capturing long-range interdependency in HSI. In this letter, we first design a local 3-D attention module to learn the spectral-spatial-channel attention by exploiting local contextual information in HSI. Then, we propose a nonlocal 3-D attention module, in which the long-range interdependency in HSI can be exploited for attention learning. By jointly embedding the local and nonlocal attention in a residual 3-D CNN, a hybrid local and nonlocal 3-D attentive CNN can be built for HSI SR. The experimental results show that local and nonlocal attention formulation leads to competitive SR performance.