PRIME: Blind Multispectral Unmixing Using Virtual Quantum Prism and Convex Geometry
Chia-Hsiang Lin, Jhao-Ting Lin

TL;DR
This paper introduces PRIME, a novel multispectral unmixing method that leverages a virtual quantum prism and convex geometry to address the underdetermined blind source separation problem in remote sensing.
Contribution
It proposes a virtual quantum prism to generate a hyperspectral image with desired properties, enabling unmixing in underdetermined scenarios, which was previously unaddressed.
Findings
Effective in underdetermined multispectral unmixing
Utilizes convex geometry for spectral unmixing
Shows promising experimental results
Abstract
Multispectral unmixing (MU) is critical due to the inevitable mixed pixel phenomenon caused by the limited spatial resolution of typical multispectral images in remote sensing. However, MU mathematically corresponds to the underdetermined blind source separation problem, thus highly challenging, preventing researchers from tackling it. Previous MU works all ignore the underdetermined issue, and merely consider scenarios with more bands than sources. This work attempts to resolve the underdetermined issue by further conducting the light-splitting task using a network-inspired virtual prism, and as this task is challenging, we achieve so by incorporating the very advanced quantum feature extraction techniques. We emphasize that the prism is virtual (allowing us to fix the spectral response as a simple deterministic matrix), so the virtual hyperspectral image (HSI) it generates has no need…
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Taxonomy
TopicsSpectroscopy Techniques in Biomedical and Chemical Research · Retinal Imaging and Analysis · Advanced Image Fusion Techniques
