Hyperspectral Pansharpening via Deep Detail Injection Network

Limited by the imagery sensors, hyperspectral images (HSIs) are characterized by their rich spectral information but poor spatial information. With the guidance of the panchromatic (PAN) images, hyperspectral pansharpening aims at achieving a HSI with both the fine spatial detail and the high spectral discrimination ability. Although many deep learning-based methods have gained great attention in recent years, it is still challenging for obtaining an appealing performance. In this paper, we propose a novel detail injection network for the hyperspectral pansharpening, which fully exploits the hierarchical features in both the low resolution HSI and the high-resolution PAN. Specifically, the low-resolution HSIs are firstly upsampled to the desired size, and make a concatenation with the PAN image to formulate a new HSI. The new HSI is sent into a residual dense network, in which residual dense block are designed to extract the abundant local features. Finally, details are injected in hierarchical levels for achieving the acceptable performance. Experimental results and data analysis on two datasets which include both indoor and outdoor scenarios have demonstrated the effectiveness of the proposed method.