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Warrant, E. (2016). Superior vision in nocturnal insects inspires new night vision technologies. SPIE Newsroom.
Abstract: Algorithms that dramatically improve the quality of video sequences captured in very dim light have been developed on the basis of the neural mechanisms in nocturnal insects with excellent visual capabilities.
Keywords: Vision; Animals; Instrumentation
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de Meester, J., & Storch, T. (2020). Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas. Sensors (Basel), 20(11).
Abstract: Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications.
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Stone, J. E., Phillips, A. J. K., Ftouni, S., Magee, M., Howard, M., Lockley, S. W., et al. (2019). Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions. Sci Rep, 9(1), 11001.
Abstract: A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other sleep schedules, including rotating shift work. Ambulatory wrist blue light irradiance and skin temperature data were collected in 16 healthy individuals on fixed and habitual sleep schedules, and 28 rotating shift workers. Artificial neural network models were trained to predict the circadian rhythm of (i) salivary melatonin on a fixed sleep schedule; (ii) urinary aMT6s on both fixed and habitual sleep schedules, including shift workers on a diurnal schedule; and (iii) urinary aMT6s in rotating shift workers on a night shift schedule. To determine predicted circadian phase, center of gravity of the fitted bimodal skewed baseline cosine curve was used for melatonin, and acrophase of the cosine curve for aMT6s. On a fixed sleep schedule, the model predicted melatonin phase to within +/- 1 hour in 67% and +/- 1.5 hours in 100% of participants, with mean absolute error of 41 +/- 32 minutes. On diurnal schedules, including shift workers, the model predicted aMT6s acrophase to within +/- 1 hour in 66% and +/- 2 hours in 87% of participants, with mean absolute error of 63 +/- 67 minutes. On night shift schedules, the model predicted aMT6s acrophase to within +/- 1 hour in 42% and +/- 2 hours in 53% of participants, with mean absolute error of 143 +/- 155 minutes. Prediction accuracy was similar when using either 1 (wrist) or 11 skin temperature sensor inputs. These findings demonstrate that the model can predict circadian timing to within +/- 2 hours for the vast majority of individuals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions.
Keywords: Human Health; Instrumentation
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Zhang, K., Zhong, X., Zhang, G., Li, D., Su, Z., Meng, Y., et al. (2019). Thermal Stability Optimization of the Luojia 1-01 Nighttime Light Remote-Sensing Camera's Principal Distance. Sensors (Basel), 19(5), 990.
Abstract: The instability of the principal distance of the nighttime light remote-sensing camera of the Luojia 1-01 satellite directly affects the geometric accuracy of images, consequently affecting the results of analysis of nighttime light remote-sensing data. Based on the theory of optical passive athermal design, a mathematical model of optical-passive athermal design for principal distance stabilization is established. Positive and negative lenses of different materials and the mechanical structures of different materials are matched to optimize the optical system. According to the index requirements of the Luojia 1-01 camera, an image-telecentric optical system was designed under the guidance of the established mathematical model. In the temperature range of -20 degrees C to +60 degrees C, the principal distance of the system changes from -0.01 mum to +0.28 mum. After on-orbit testing, the geometric accuracy of the designed nighttime light remote-sensing camera is better than 0.20 pixels and less than index requirement of 0.3 pixels, which indicating that the principal distance maintains good stability on-orbit and meets the application requirements of nighttime light remote sensing.
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Zhang, G., Li, L., Jiang, Y., Shen, X., & Li, D. (2018). On-Orbit Relative Radiometric Calibration of the Night-Time Sensor of the LuoJia1-01 Satellite. Sensors (Basel), 18(12).
Abstract: The LuoJia1-01 satellite can acquire high-resolution, high-sensitivity nighttime light data for night remote sensing applications. LuoJia1-01 is equipped with a 4-megapixel CMOS sensor composed of 2048 x 2048 unique detectors that record weak nighttime light on Earth. Owing to minute variations in manufacturing and temporal degradation, each detector's behavior varies when exposed to uniform radiance, resulting in noticeable detector-level errors in the acquired imagery. Radiometric calibration helps to eliminate these detector-level errors. For the nighttime sensor of LuoJia1-01, it is difficult to directly use the nighttime light data to calibrate the detector-level errors, because at night there is no large-area uniform light source. This paper reports an on-orbit radiometric calibration method that uses daytime data to estimate the relative calibration coefficients for each detector in the LuoJia1-01 nighttime sensor, and uses the calibrated data to correct nighttime data. The image sensor has a high dynamic range (HDR) mode, which is optimized for day/night imaging applications. An HDR image can be constructed using low- and high-gain HDR images captured in HDR mode. Hence, a day-to-night radiometric reference transfer model, which uses daytime uniform calibration to calibrate the detector non-uniformity of the nighttime sensor, is herein built for LuoJia1-01. Owing to the lack of calibration equipment on-board LuoJia1-01, the dark current of the nighttime sensor is calibrated by collecting no-light desert images at new moon. The results show that in HDR mode (1) the root mean square of mean for each detector in low-gain (high-gain) images is better than 0.04 (0.07) in digital number (DN) after dark current correction; (2) the DN relationship between low- and high-gain images conforms to the quadratic polynomial mode; (3) streaking metrics are better than 0.2% after relative calibration; and (4) the nighttime sensor has the same relative correction parameters at different exposure times for the same gain parameters.
Keywords: Instrumentation; Remote Sensing
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