Active and Low-Cost Hyperspectral Imaging for the Spectral Analysis of a Low-Light Environment

Author:

Tang Yang12ORCID,Song Shuang12ORCID,Gui Shengxi12ORCID,Chao Weilun34,Cheng Chinmin24,Qin Rongjun1234ORCID

Affiliation:

1. Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, OH 43210, USA

2. Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA

3. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA

4. Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA

Abstract

Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system’s hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system’s hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining.

Funder

The Translational Data Analytics Institute Pilot seed grant

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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