Affiliation:
1. The Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Abstract
Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method.
Funder
National Natural Science Foundation of China
Ministry of Education of China
Xi’an University of Technology
Subject
General Earth and Planetary Sciences