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

Yang Yu · Erting Pan · Xinya Wang · Yuheng Wu · Xiaoguang Mei · Jiayi Ma

Arch 4A-E Poster #436
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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:

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