Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-based Hyperspectral Image Synthesis

In the realm of AI data serves as a pivotal resource. Real-world hyperspectral images (HSIs) bearing wide spectral characteristics are particularly valuable. However the acquisition of HSIs is always costly and time-intensive resulting in a severe data-thirsty issue in HSI research and applications. Current solutions have not been able to generate a sufficient volume of diverse and reliable synthetic HSIs. To this end our study formulates a novel generalized paradigm for HSI synthesis i.e. unmixing before fusion that initiates with unmixing across multi-source data and follows by fusion-based synthesis. By integrating unmixing this work maps unpaired HSI and RGB data to a low-dimensional abundance space greatly alleviating the difficulty of generating high-dimensional samples. Moreover incorporating abundances inferred from unpaired RGB images into generative models allows for cost-effective supplementation of various realistic spatial distributions in abundance synthesis. Our proposed paradigm can be instrumental with a series of deep generative models filling a significant gap in the field and enabling the generation of vast high-quality HSI samples for large-scale downstream tasks. Extension experiments on downstream tasks demonstrate the effectiveness of synthesized HSIs. The code is available at: HSI-Synthesis.github.io.