Multispectral Measurements Research Articles

HDR Multispectral Imaging-based BRDF Measurement using a Flexible Robotic Arm System

Materials with special appearance properties such as goniochromatic materials require complex bidirectional measurements to properly characterise their colour and gloss. Normally, these measurements are performed by goniospectrophotometers which are expensive and not commonly available. In this paper a flexible imaging system composed of a snapshot multispectral camera and a light source attached to a robotic arm, is used to obtain HDR BRDF measurements of patinas commonly used in cultural heritage objects. The system is evaluated by comparing the results to those of a commercially available goniospectrophotometer. It is found that with a known uncertainty, the system is capable of producing accurate measurements of samples with a roughness equal or lower than 6.19 μm. For roughnesses higher that 12.48 μm, the accuracy of the system decreases. Moreover, it is found that the size and orientation of the region of interest plays a great influence on the precision of the imaging system.

Development of a multispectral stereo-camera system comparable to Hayabusa2 optical navigation camera (ONC-T) for observing samples returned from asteroid (162173) Ryugu

Hayabusa2 collected 5.4 g of samples from the asteroid (162173) Ryugu and brought them back to Earth. Obtaining multiband images of these samples with the spectral bands comparable to those used for remote-sensing observations is important for characterizing the collected samples and examining how representative the sample is compared with spacecraft observations of Ryugu as a whole. In this study, we constructed a multiband microscopic camera system that enables both visual multispectral imaging at 390 (ul), 475 (b), 550 (v), 590 (Na), 700 (w), and 850 nm (x), and three-dimensional (3D) shape reconstruction of individual grain samples based on stereo imaging. The imaging system yields the images of 4096 x 2160 pixels with the pixel resolution of 1.93 {\mu}m/pix and field of view of 7.9 x 4.2 mm. Our validation measurements demonstrate that our multispectral imaging system, which observes the samples with the spectral bands comparable to those on the telescopic optical navigation camera (ONC-T) on Hayabusa2, yields reflectance spectra with a relative error of 3% and a 3D model with an error of 5%. These results indicate that the multiband imaging system with a 3D shape reconstruction capability yields accurate spectral and shape data on the returned samples. Using this instrument, we conducted multispectral measurements of two Ryugu samples (grains in the dishes A3 and C1) acquired from two locations on the asteroid. The average spectra of the measured Ryugu samples were flat and consistent with the global averaged spectrum of Ryugu. The 550-nm band (v-band) reflectance of the returned grains in the dishes was 2.4% on average, higher than that of the global averaged spectrum of Ryugu observed with ONC-T. This apparent difference could be because the returned grains have greater specular reflectance.

Multi-spectral radiation thermometry based on an Alpha spectrum-LM algorithm under the background of high temperature and intense reflection.

In order to meet the needs of multi-spectral radiation temperature measurement under high temperature background, this paper studies the problems of reflected radiation interference and spectral emissivity difficult to obtain in high temperature and intense reflection environment. First, using discrete triangular surface elements and radiation angle coefficients, an analysis model of high temperature background reflected radiation is constructed to describe the variation characteristics of high temperature background reflected radiation. Secondly, the least squares support vector machine (LSSVM) is optimized by particle swarm optimization (PSO) algorithm, and an emissivity model identification algorithm based on Alpha spectrum-Levenberg Marquarelt (LM) algorithm is proposed, which has stronger applicability and accuracy than existing emissivity model identification methods. Finally, the high temperature background radiation and the emissivity model are combined to construct and solve the multi-spectral target equation, so as to realize the reflected radiation error correction and radiation temperature measurement under the high temperature and intense reflection background. The simulation and experimental comparison with the existing methods show that the temperature measurement error of the radiation temperature measurement method proposed in this paper is below 9.5K, which can effectively correct the reflected radiation error and further improve the temperature measurement accuracy.

Mapping of coastal surface chlorophyll-a concentration by multispectral reflectance measurement from unmanned aerial vehicles

In subtropical coastal waters, the explosive growth of phytoplankton under favorable conditions can lead to water discolouration and massive fish kills. Manual field sampling and laboratory analysis of chlorophyll-a concentration (Chl-a) as an indicator to algal biomass, is resources intensive and time consuming, delaying responses to disastrous harmful algal blooms. Cloudy weather often precludes the use of satellite images for water quality and algal bloom monitoring. This study aims at developing an estimator algorithm for quantitative mapping of surface Chl-a for coastal waters, based on surface reflectance measurement from an Unmanned Aerial Vehicle (UAV) with a five-band multispectral camera. The surface reflectance is obtained from calibrated multispectral images which are radiometric-corrected against incoming solar radiation. It is found that Chl-a has an inverse correlation with the Normalized Green-Red Difference Index (NGRDI). A regression estimator model for Chl-a from NGRDI is developed, showing excellent performance for fish farms in coastal waters with different characteristics. The technology is demonstrated for mapping the spatial and temporal variation of Chl-a during an algal bloom, offering a useful complement to traditional field monitoring for fisheries management and emergency response.

Spectral Light Curve Simulation for Parameter Estimation from Space Debris

Characterisation of space debris has become a fundamental task to facilitate sustainable space operations. Ground-based surveillance provides the means to extract key attributes from spacecraft. However, signal inversion attempts are generally under-constrained, which is why an increase in measurement channels through multispectral observations is expected to benefit parameter estimation. The current approach to simulating space debris observation at the Institute of Technical Physics of the German Aerospace Centre (DLR) in Stuttgart relies on monochromatic images taken from the POV-Ray render engine to form light curve signals. Rendered scenes are generated based on the location of an observer by propagating a target’s orbit and rotation. This paper describes the simulation of spectral light curves through the extension of DLR’s Raxus Prime simulation environment. Light reflections are computed using the Mitsuba2 spectral render engine, while atmospheric attenuation is accounted for by the radiative transfer library libRadTran. A validation of the simulator was achieved using multispectral measurements, carried out at the Uhlandshöhe research observatory in Stuttgart. Measured and synthetic data were found to be in agreement based on an RMS error <1% of the total measured signal count. Further, simulated spectral products were used to determine a target’s surface material composition and rotation state and examine aspects of laser ranging to non-cooperative targets.

Multispectral athermal fading rate measurements of K-feldspar

This study reports athermal fading rates in K-feldspar grains extracted from sediments of varied ages and provenances. Multiple combinations of stimulation and emission spectral regions were examined to identify an optimum combination that provides a luminescence signal with minimal athermal fading. Stimulation wavelengths used were IR (855 ± 33 nm), green (525 ± 30 nm), blue (470 ± 20 nm), and violet (405 ± 15 nm), and detection windows were broad-UV (260–400 nm), narrow-UV (327–353 nm), and blue (320–520 nm).Athermal fading rates using a single stimulation and sequential double stimulation combinations were estimated. For single stimulation, the average fading rates (gAV) ranged from 6.6 to 7.9% per decade. Sequential double stimulation comprising post green-blue (pGB), post blue-violet (pBV), post blue-IR (pBIR), and post violet-IR (pVIR) gave fading rates ranging from 2.0 to 0.0% per decade. The minimum fading rate value gAV = 0.0 ± 0.1% per decade was obtained for pVIR stimulation, and this highlights it as a potential candidate for dating sediments such that the tedium and time of fading measurements can be minimized.

Nondestructive Detection Method for the Calcium and Nitrogen Content of Living Plants Based on Convolutional Neural Networks (CNN) Using Multispectral Images

Herein, we present the novel method targeted for determination of plant nutritional state with the use of computer vision and Neural Networks. The method is based on multispectral imaging performed by an exclusively designed Agroscanner and a dedicated analytical system for further data analysis with Neural Networks. An Agroscanner is a low-cost mobile construction intended for multispectral measurements at macro-scale, operating at four wavelengths: 470, 550, 640 and 850 nm. Together with developed software and implementation of a Neural Network it was possible to design a unique approach to process acquired plant images and assess information about plant physiological state. The novelty of the developed technology is focused on the multispectral, macro-scale analysis of individual plant leaves, rather than entire fields. Such an approach makes the method highly sensitive and precise. The method presented herein determines the basic physiological deficiencies of crops with around 80% efficiency.

Multispectral pattern recognition measures change in drusen area in age-related macular degeneration with high congruency to expert graders.

Drusen are a hallmark lesion of age-related macular degeneration (AMD) and changes in their area and/or volume are strongly associated with disease progression. Assessment of longitudinal change in drusen size in clinical practice however is limited to a single commercial tool or manual inspection by clinicians. In this study we analysed change in drusen area in 33 eyes with intermediate AMD across two separate visits using a novel technique known as multispectral pattern recognition for en face retinal images from various imaging modalities (infrared (815 nm), fundus autofluorescence (488 nm) and green (532 nm) scanning laser ophthalmoscopy). We found 91% (30/33 eyes) agreement in the direction of drusen change for multispectral pattern recognition relative to expert graders who graded eyes as having drusen progression, regression or being stable. Multispectral pattern recognition showed 100% sensitivity (22/22 eyes) and 73% specificity (8/11 eyes). In comparison, we found only 70% (23/33 eyes) agreement in the direction of drusen change with a commercially available change analysis software, the Cirrus Advanced RPE Analysis relative to expert graders, with a sensitivity 64% (14/22 eyes) and specificity of 82% (9/11 eyes). Total drusen area or amount of change between visits had no significant effect on agreement. This suggests multispectral pattern recognition can quantify longitudinal change in drusen area from multimodal imaging with greater congruency to expert graders than a commercially available platform based on a single imaging modality. Considering the association of drusen area and disease progression, this method could aid clinical assessment and monitoring of AMD.

Synchronized Two-Color Time-Resolved Dual-Comb Spectroscopy for Quantitative Detection of HOx Radicals Formed from Criegee Intermediates.

Criegee intermediates, derived from ozonolysis of alkenes and recognized as key species in the production of nonphotolytic free radicals, play a crucial role in atmospheric chemistry. Here, we present a spectrometer based on synchronized two-color time-resolved dual-comb spectroscopy, enabling simultaneous spectral acquisitions in two molecular fingerprint regions near 2.9 and 7.8 μm. Upon flash photolysis of CH2I2/O2/N2 gas mixtures, multiple reaction species, involving the simplest Criegee intermediates (CH2OO), formaldehyde (CH2O), hydroxyl (OH) and hydroperoxy (HO2) radicals are simultaneously detected with microsecond time resolution. The concentration of each molecule can be determined based on high-resolution rovibrational absorption spectroscopy. With quantitative detection and simulation of temporal concentration profiles of the targeted molecules at various conditions, the underlying reaction mechanisms and pathways related to the formation of the HOx radicals, which can be generated from decomposition of initially energized and vibrationally excited Criegee intermediates, are explored. This approach capable of achieving multispectral measurements with simultaneously high spectral and temporal resolutions opens up the opportunities for quantification of transient intermediates and products, thus, enabling elucidation of complex reaction mechanisms.

Spatial Regression Models for Field Trials: A Comparative Study and New Ideas.

Naturally occurring variability within a study region harbors valuable information on relationships between biological variables. Yet, spatial patterns within these study areas, e.g., in field trials, violate the assumption of independence of observations, setting particular challenges in terms of hypothesis testing, parameter estimation, feature selection, and model evaluation. We evaluate a number of spatial regression methods in a simulation study, including more realistic spatial effects than employed so far. Based on our results, we recommend generalized least squares (GLS) estimation for experimental as well as for observational setups and demonstrate how it can be incorporated into popular regression models for high-dimensional data such as regularized least squares. This new method is available in the BioConductor R-package pengls. Inclusion of a spatial error structure improves parameter estimation and predictive model performance in low-dimensional settings and also improves feature selection in high-dimensional settings by reducing “red-shift”: the preferential selection of features with spatial structure. In addition, we argue that the absence of spatial autocorrelation (SAC) in the model residuals should not be taken as a sign of a good fit, since it may result from overfitting the spatial trend. Finally, we confirm our findings in a case study on the prediction of winter wheat yield based on multispectral measurements.

Spectral indices for estimating total dissolved solids in freshwater wetlands using semi-empirical models. A case study of Guartinaja and Momil wetlands

ABSTRACT Water is one of the most important and abundant resources on earth; therefore, determining easier and more accurate methods of measuring water quality has become challenging in recent years. Images of the Sentinel-2 multispectral (MSI) sensor and field measurements for three seasons and 39 spectral indices were evaluated to identify those that can be used as patterns for the generation of semi-empirical bio-optical models to predict the total dissolved solids (TDS) in surface water of freshwater wetlands. The Guartinaja and Momil wetlands, located in the Wetland Complex of Bajo Sinú, Northern Colombia, were used as references in the field. The individual bands of the Sentinel 2 MSI sensor and the spectral indices derived from these bands were tested (and will be referred to in this paper as spectral indices). They were classified as follows: 10 individual spectral bands of the MSI sensor of the Sentinel 2 satellite; 11 indices reported by the literature to determine water, vegetation, or soils; 11 indices reported to estimate salinity; and 7 to estimate total dissolved solids and proposed in this research. The indices were evaluated based on three exclusive conditions: 1) the correlation between the TDS values measured in the field and indices spectral values >0.7 for at least two sampling seasons; 2) for the three seasons, the regression models derived from the indices have a determination coefficient >0.7; and 3) the spatial correlation matrix between the images derived from the models is >0.8 for at least two out of the three analysis seasons. We achieved important results: 1) seven indices (B3, Brightness-3, SI-3, SI-5, Ferdous-2020, TDS-1, and TDS5) met the three conditions proposed above and therefore, they were preliminarily defined as patterns for estimating TDS in the selected wetlands; 2) Additionally, this research provided two new efficient indices for calculating TDS through bio-optical models: TDS-1 and TDS-5. The validation of the regression models derived from these indices indicated a high accuracy of the prediction surfaces, expressed in values <10% of the normalized root-mean-square deviation. The results of this study contribute to the determination of semi-empirical bio-optical models for predicting optically inactive water quality parameters, which are generally predicted with empirical models whose use is spatiotemporally limited. Additionally, the research contributions that are carried out to improve the methods of evaluating water quality from remote sensing products become investigations that positively impact the evaluation of water resources, especially in geographical locations in which there is no with sufficient financial resources for on-site sampling.

Semantic segmentation of multispectral photoacoustic images using deep learning

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

A study on surface-temperature measurement by means of a vacuum-arc cathode under different current conditions

Accurate detection and analysis of the cathode surface temperature delivers an important reference value as regards the prediction of electrode life and corrosion degree, a thorough study of the vacuum-breakdown phenomenon, design and application of the vacuum-arc ion source and vacuum switch. In this paper, multispectral true temperature measurement T of the surface temperature field of pulsed vacuum arc cathode materials under different discharge currents is carried out. Firstly, aiming at solving the problem of synchronous imaging measurement of the temperature field across the cathode of pulsed vacuum arc, a multi-wavelength monochromatic image-acquisition optical path is designed, based upon the image acquisition module of a high-speed monochrome camera. Also, multiple monochromatic spectral radiation images of the target are synchronously acquired on the focal plane of the same monochrome camera detector. As a result, the spectral-radiation image information of multiple wavelengths can be obtained in a single exposure time, which greatly shortens the temperature-field image acquisition time. Secondly, based upon the multispectral true-temperature calculation method, high-speed multispectral imaging measurement of the target temperature field is realized. Finally, the two-dimensional true temperature distribution of the cathode temperature field under different current conditions was studied by setting up an experimental platform for measuring the cathode temperature of the vacuum arc under different current conditions.

Temperature Inversion Algorithm for Multi-Spectral Measurement of Material Shock Radiation Characteristics

Temperature Inversion Algorithm for Multi-Spectral Measurement of Material Shock Radiation Characteristics

Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data

As the successors of the HuanjingJianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HJ-2 A/B satellites have been successfully launched on the September 27 of 2020. The Polarized Scanning Atmospheric Corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring high speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network based AOD retrieval model (named the AODNet), which takes full advantage of the multispectral measurements of PSAC for AOD retrieval with a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either of Sun-sky radiometer Observation NETwork (SONET) AOD or AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85 and more than 60% of the AODNet AOD can fall into the expected error envelope of &#x00B1;(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS Dark Target (DT) and Deep Blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.

Multispectral Mid-Infrared Camera System for Accurate Stand-Off Temperature and Column Density Measurements on Flames.

Accurate measurement of temperature in flames is a challenging problem that has been successfully addressed by hyperspectral imaging. This technique is able to provide maps of not only temperature T (K) but also of column density Q (ppmm) of the main chemical species. Industrial applications, however, require cheaper instrumentation and faster and simpler data analysis. In this work, the feasibility and performance of multispectral imaging for the retrieval of T and Q in flames are studied. Both the hyperspectral and multispectral measurement methods are described and applied to a standard flame, with known T and Q, and to an ordinary Bunsen flame. Hyperspectral results, based on emission spectra with cm resolution, were found in previous works to be highly accurate, and are thus considered as the ground truth to compare with multispectral measurements of a mid-IR camera (3 to 5 m) with a six interference filter wheel. Maps of T and Q obtained by both methods show that, for regions with T K, the average of relative errors in multispectral measurements is ∼5% for T (and can be reduced to ∼2.5% with a correction based on a linear regression) and ∼20% for Q. Results obtained with four filters are very similar; results with two filters are also similar for T but worse for Q.

A new methodology for inferring surface ozone from multispectral satellite measurements

Over the past two decades, satellite instruments have provided unprecedented information on global air quality, and yet the remote sensing of surface ozone remains elusive. Here we propose a new method to infer spatial variability in surface ozone by combining multispectral ozone retrievals using radiances from the tropospheric emission spectrometer thermal infrared instrument and the ozone monitoring instrument ultratraviolet/visible instrument with a chemical reanalysis. We find that our inferred surface ozone in summertime China and the United States has regional biases of less than 4 ppb and a high spatial correlation when validated against independent surface measurements. Over the broader Asia region, our analysis results in a spatial pattern of summertime surface ozone that can largely be explained by a combination of the Asian monsoon circulation and emissions. Our results show the potential of combining satellite measurements and chemical reanalyses to provide critical air quality information in regions of limited surface networks, thereby enhancing the global air quality observing system.

Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation

A new perspective for studying Earth processes has been soundly demonstrated by the Deep Space Climate Observatory (DSCOVR) mission. For the past 6 years, the first Earth-observing satellite orbiting at the Lagrange 1 (L1) point, the DSCOVR satellite has been viewing the planet in a fundamentally different way compared to all other satellites. It is providing unique simultaneous observations of nearly the entire sunlit face of the Earth at a relatively high temporal resolution. This capability enables detailed coverage of evolving atmospheric and surface systems over meso- and large-scale domains, both individually and as a whole, from sunrise to sunset, under continuously changing illumination and viewing conditions. DSCOVR’s view also contains polar regions that are only partially seen from geostationary satellites (GEOs). To exploit this unique perspective, DSCOVR instruments provide multispectral imagery and measurements of the Earth’s reflected and emitted radiances from 0.2 to 100 µm. Data from these sensors have been and continue to be utilized for a great variety of research involving retrievals of atmospheric composition, aerosols, clouds, ocean, and vegetation properties; estimates of surface radiation and the top-of-atmosphere radiation budget; and determining exoplanet signatures. DSCOVR’s synoptic and high temporal resolution data encompass the areas observed during the day from low Earth orbiting satellites (LEOs) and GEOs along with occasional views of the Moon. Because the LEO and GEO measurements can be easily matched with simultaneous DSCOVR data, multiangle, multispectral datasets can be developed by integrating DSCOVR, LEO, and GEO data along with surface and airborne observations, when available. Such datasets can open the door for global application of algorithms heretofore limited to specific LEO satellites and development of new scientific tools for Earth sciences. The utility of the integrated datasets relies on accurate intercalibration of the observations, a process that can be facilitated by the DSCOVR views of the Moon, which serves as a stable reference. Because of their full-disc views, observatories at one or more Lagrange points can play a key role in next-generation integrated Earth observing systems.

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters.

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

Multispectral fluorescence lifetime imaging device with a silicon avalanche photodetector.

We report the design, development, and characterization of a novel multi-spectral fluorescence lifetime measurement device incorporating solid-state detectors and automated gain control. For every excitation pulse (∼1 µJ, 600 ps), this device records complete fluorescence decay from multiple spectral channels simultaneously within microseconds, using a dedicated UV enhanced avalanche photodetector and analog to digital convert (2.5 GS/s) in each channel. Fast (<2 ms) channel-wise dynamic range adjustment maximizes the signal-to-noise ratio. Fluorophores with known lifetime ranging from 0.5-6.0 ns were used to demonstrate the device accuracy. Current results show the clear benefits of this device compared to existing devices employing microchannel-plate photomultiplier tubes. This is demonstrated by 5-fold reduction of lifetime measurement variability in identical conditions, independent gain adjustment in each spectral band, and 4-times faster imaging speed. The use of solid-state detectors will also facilitate future improved performance and miniaturization of the instrument.