Line Scan Hyperspectral Imaging Framework for Open Source Low-Cost Platforms

With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the needed processing power. In this paper, we develop a novel theoretical framework required for an open source ultra-low-cost hyperspectral imaging platform based on the line scan method suitable for remote sensing applications. Then, we demonstrate the design and fabrication of an open source platform using consumer-grade commercial off-the-shelf components that are both affordable and easily accessible to researchers and users. At the heart of the optical system is a consumer-grade spectroscope along with a basic galvanometer mirror that is widely used in laser scanning devices. The utilized pushbroom scanning method provides a very high spectral resolution of 2.8 nm, as tested against commercial spectral sensors. Since the resolution is limited by the slit width of the spectroscope, we also provide a deconvolution method for the line scan in order to improve the monochromatic spatial resolution. Finally, we provide a cost-effective testing method for the hyperspectral imaging platform where the results validate both the spectral and spatial performances of the platform.

Automated summary

With advancements in computer processing power and deep learning techniques, hyperspectral imaging is being explored for improved sensing applications. In this paper, a novel theoretical framework and an open source ultra-low-cost hyperspectral imaging platform based on the line scan method are developed. The platform is designed and fabricated using consumer-grade components, providing high spectral resolution and improved spatial resolution. A cost-effective testing method is also provided to validate the platform's performance.