# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 5 Conclusions  
In this work, a novel spectral super-resolution network, called the learning spectral transformer network (LSTNet), was proposed, in which the spectral structure of a reference HSI is utilized through transfer learning to promote a reasonable reconstruction spectrum. The main contributions of the work can be summarized as follows:  
1. The proposed method is composed of an SRM (spectral reconstruction module) and an STM (spectral transformer module) to learn the prior spectral information and reference spectrum.
2. The STM (spectral transformer module) is used to compute the spatial relevance between the input MSI and reference MSI, and the related spectral structures are transferred from the reference HSI into the spectral reconstruction network.
3. In the SRM (spectral reconstruction module), the channel attention mechanism is used to enhance the channel-wise adaptive learning, through which the model can effectively reallocate weights along the spectral dimension. The proposed network is capable of transferring the spectral information from the reference image and adaptively adjusting the weights of the features.  
Extensive experiments conducted on two simulated benchmark data sets and a real data pair demonstrated the effectiveness of the proposed method compared to state-of-the-art methods, along with its ability to produce highly accurate reconstructed images.  
**Challenges and Trends**: Due to the limitations of the imaging system, a hyperspectral imaging system usually needs to make trade-offs in field of view, focal length, and imaging CCD size to meet the high imaging signal-to-noise ratio. This makes it difficult for the hyperspectral images acquired by the current satellite to simultaneously meet the usage requirements of wide swath and high spatial resolution. Enormous efforts have been made to develop hyperspectral image reconstruction techniques, such as spatio-spectral fusion, single image super-resolution, and spectral super-resolution. The major remaining issue in the field of spatio-spectral fusion is the development of more accurate fusion methods for the inconsistency between coarse and fine spatial resolution images caused by the atmospheric conditions or the acquisition geometry. The challenge of single-image super-resolution reconstruction is the effectiveness of spatial information reconstruction at larger scaling factors rather than simply sharpening ground object edges. The main challenge for spectral super-resolution reconstruction is to develop reconstruction methods with more spectral fidelity and interpretability.  
[PERSON] was primarily responsible for mathematical modeling and experimental design. [PERSON] and [PERSON] contributed to the original idea for the proposed methods and to the experimental analysis. [PERSON] improved the mathematical model and revised the paper. [PERSON] and [PERSON] provided support regarding the application of the proposed method. [PERSON] completed the theoretical framework. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under grant NO. 52174610 and the Natural Science Foundation of Hebei Province under grant NO. EE2020402086.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.