In this paper, a high-speed hyperspectral target detection system based on high-efficiency spectrographs and illumination devices is proposed. The system includes a hyperspectral imager, an illumination system, and a data processing system with spectral target recognition. The system can be used for fast impurity rejection on industrial lines. By adopting a high diffraction efficiency grating and a low distortion spectral spectroscopic system, the system realizes spectral imaging with high throughput and low distortion. Compact linear light source is used to achieve high irradiance full-spectrum illumination. The edge computing system adopts a spectral target recognition method combining CTBS with RTCEM and RTRAD. The spectral range of the system is 400 nm to 1000nm, the spectral resolution is 5 nm. The system can be used for the assembly line with a transportation speed of 1m/s, and the unknown debris detection with an accuracy of more than 87% and the known debris detection with an accuracy of 96% can be carried out on debris with a size of less than 2 mm. The method proposed in this paper increases the detection speed of existing hyperspectral detection systems by more than three times, which is expected to improve the practicability of hyperspectral detection technology in the field of industrial production.
The atmosphere calibrated airborne and space borne hyperspectral images are the HDRF of canopy. The spatial nonuniformity of HDRF may result in inversion errors of the heavy metal stressing. In this paper, the HDRF of copper stressed plant samples under different illumination conditions was acquired with the laboratory hyperspectral simulation system called MHRS2F. The difference between the HDRF of canopy and the BCRF of leaves was firstly discussed. Then the changes of spatial distribution of the HDRF for different copper concentrations and illumination conditions were discussed. At last, the sensitivity of various vegetation indices to illumination and observation directions was compared. By comparing the prediction accuracy of different vegetation indices on different observation directions and illumination conditions, the HVI and mRENDVI were found to be more stable and accurate.
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