Unsupervised Deep Adaptive Learning Spatial Reconstruction Network Based on Hyperspectral Data Fusion

Due to limitations of satellite imaging systems, hyperspectral image (HSI) often suffers from incomplete coverage, with certain regions of the study area missing. Data fusion and reconstruction are effective approaches to resolve the contradiction in spatial and spectral domains, where related theories have intensively developed in recent years. However, existing fusion methods are mostly applicable to simulated data and are challenging to apply to real data. In this paper, we propose an unsupervised fusion spatial reconstruction network namely UFSRnet, which not only reconstructs the missing regions of HSI but also learns the differences between heterogeneous data adaptively. Specifically, a sensor radiation deviation correction (SRDC) module is designed to tackle the disparities between heterogeneous data adaptively. The model demonstrates commendable performance across both simulated and real data sets.