2021
Hyperspectral images (HSIs) are the main data type dealt in hyperspectral imaging. However, in real-world situations the quality of HSIs is circumscribed by the design of present hyperspectral cameras and photoing conditions, making the captured HSIs far from satisfactory. Considering present methods do not fully exploit the 3D nature of HSIs, we choose to build a spatial-spectral distinguishment mechanism by using a multi-scale image pyramid model, which is used later to design a pyramid module for feature extraction. The module could learn spatial and spectral features separately on different stages of its image pyramid, making the learning process of super-resolution much easier. Then we employ that module to build an end-to-end network model via residual learning. Experiments and ablation studies have proved that our model produced good objective and subjective results compared to present methods on hyperspectral remote sensing datasets.
