Improving Optical Measurements: Non-Linearity Compensation of Compact Charge-Coupled Device (CCD) Spectrometers

Nehir, Münevver; Frank, Carsten; Aßmann, Steffen; Achterberg, Eric P.

doi:10.3390/s19122833

Cited by 22 publications

(13 citation statements)

References 21 publications

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“…In addition, the ELM approach is based on the assumption that reflectance is linearly related to upwelling radiance. Non-linearity is a known effect in spectroradiometers: meaning an increasing number of photons does not mean the same proportional increase of digital numbers captured by the detector [ 37 , 41 ]. However, non-linearity is strongest at the low and high ends of the dynamic range of the detector.…”

Section: Discussionmentioning

confidence: 99%

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

Peng

Cendrero-Mateo

Bendig

et al. 2022

Sensors

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Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy’s structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun–canopy–sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m−2 sr−1 nm−1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m−2 sr−1 nm−1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m−2 sr−1 nm−1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants’ photosynthetic capacity.

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Section: Discussionmentioning

confidence: 99%

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

Peng

Cendrero-Mateo

Bendig

et al. 2022

Sensors

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“…The number of counts obtained from the spectrometer was corrected for the nonlinearity of the detector. 38 Next, the number of counts (I sample ) was converted to an absolute reflectance using:…”

Section: Discussionmentioning

confidence: 99%

Experimental validation of a recently developed model for single-fiber reflectance spectroscopy

et al. 2021

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Significance: We recently developed a model for the reflectance measured with (multi-diameter) single-fiber reflectance (SFR) spectroscopy as a function of the reduced scattering coefficient μ 0 s , the absorption coefficient μ a , and the phase function parameter p sb. We validated this model with simulations. Aim: We validate our model experimentally. To prevent overfitting, we investigate the wavelength-dependence of p sb and propose a parametrization with only three parameters. We also investigate whether this parametrization enables measurements with a single fiber, as opposed to multiple fibers used in multi-diameter SFR (MDSFR). Approach: We validate our model on 16 phantoms with two concentrations of Intralipid-20% (μ 0 s ¼ 13 and 21 cm −1 at 500 nm) and eight concentrations of Evans Blue (μ a ¼ 1 to 20 cm −1 at 605 nm). We parametrize p sb as 10 −5 • ðp 1 ðλ∕650Þ þ p 2 ðλ∕650Þ 2 þ p 3 ðλ∕650Þ 3 Þ. Results: Average errors were 7% for μ 0 s , 11% for μ a , and 16% with the parametrization of p sb ; and 7%, 17%, and 16%, respectively, without. The parametrization of p sb improved the fit speed 25 times (94 s to <4 s). Average errors for only one fiber were 50%, 33%, and 186%, respectively. Conclusions: Our recently developed model provides accurate results for MDSFR measurements but not for a single fiber. The p sb parametrization prevents overfitting and speeds up the fit.

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“…Currently, CCD sensors are widely used in machine vision [64][65][66]. CMOS image sensors are still in their early stages and yet to mature [67,68].…”

Section: Ccd and Cmosmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

356

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Improving Optical Measurements: Non-Linearity Compensation of Compact Charge-Coupled Device (CCD) Spectrometers

Cited by 22 publications

References 21 publications

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

Experimental validation of a recently developed model for single-fiber reflectance spectroscopy

State of the Art in Defect Detection Based on Machine Vision

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