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

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.

A Novel Correction Methodology to Improve the Performance of a Low-Cost Hyperspectral Portable Snapshot Camera

“…Generally, the HI at 800 nm appears more uniform than the HIs at 470 and 600 nm. The lower image uniformity in the edge spectral regions and the spectral jumps at 513 and 650 nm could be interpreted as an effect of the presence of different CMOS sensors, affecting the acquisition of sharpness HIs in the edge spectral regions [ 33 ].…”

“…Nevertheless, CMOS sensors show higher sensitivity and low power consumption but also higher background noise and dark current [ 32 ]. The PHCs collect images using the natural light, and the imaging system cannot function in a dark or low-light environment [ 33 ]. Despite the technological developments of the low-cost PHCs, critical aspects are reported during acquisition and data processing, such as the long instrumental warm-up time, the presence of spectral discontinuities in correspondence of the spectral border region of different CMOS sensors, signal jumps, and high levels of background noise.…”

Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification

“…This allows for the identification of complicated spectral characteristics of subjects that might be unnoticed. HSI is extensively used in various earth remote sensing applications, including land use and land cover classification 1 , precision agriculture 2,3 , object detection 4 , tree species classification 5 , brain cancer detection 6 , and more.…”

Hyperspectral upgrade solution for biomicroscope combined with Transformer network to classify infectious bacteria

“…Furthermore, the outstanding performance of this system has been successfully demonstrated by the authors through three experiments: food freshness detection, comparison between real, and printed leaves, and rock classification. This success serves as compelling evidence of the significant potential of the system in practical applications [22].…”