Unsupervised Hyperspectral and Multispectral Image Fusion Based on Deep Adaptive Attention Network

Due to the limitations of satellite imaging hardware, it is more demand to fuse hyperspectral image (HSI) and multispectral image (MSI) in an unsupervised manner in real application scenarios. However, current research has certain limitations, either relying on more prior knowledge or lack of verification of model performance with real satellite data. In order to solve the above problems, this paper designs a deep unsupervised adaptive attention network (DUAA-Net). This model can adaptively learn the mapping relationship between HSI and MSI. Compared with related unsupervised deep learning methods, this model not only simulates data achieved better indicators but also more accurate results in classification applications of real data.