Hyperspectral System Research Articles

Near-field terahertz electro-optical imaging based on a polarization image sensor

Abstract This paper presents a hyperspectral microscopy system that offers two-dimensional (2D) measurement of the spectral phase and amplitude information of terahertz (THz) radiation without the need for raster scanning. To achieve this, a new THz imaging method is introduced, wherein the distribution of the THz electric field is spatially measured using the electro-optic effect with a commercial polarization image sensor. This method enables the direct measurement of polarization components, eliminating the need for the polarization optics usually required in conventional electro-optical imaging. The performance of this imaging method is compared with a conventional 2D imaging system based on a standard visible camera. Finally, the sub-wavelength resolution capabilities of this new sensor are demonstrated by imaging a sample in the near field.

Evaluation of Focus Measures for Hyperspectral Imaging Microscopy Using Principal Component Analysis.

An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400-1100 nm) and near-infrared (900-1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context.

Development and Application of Unmanned Aerial High-Resolution Convex Grating Dispersion Hyperspectral Imager.

This study presents the design and development of a high-resolution convex grating dispersion hyperspectral imaging system tailored for unmanned aerial vehicle (UAV) remote sensing applications. The system operates within a spectral range of 400 to 1000 nm, encompassing over 150 channels, and achieves an average spectral resolution of less than 4 nm. It features a field of view of 30°, a focal length of 20 mm, a compact volume of only 200 mm × 167 mm × 78 mm, and a total weight of less than 1.5 kg. Based on the design specifications, the system was meticulously adjusted, calibrated, and tested. Additionally, custom software for the hyperspectral system was independently developed to facilitate functions such as control parameter adjustments, real-time display, and data preprocessing of the hyperspectral camera. Subsequently, the prototype was integrated onto a drone for remote sensing observations of Spartina alterniflora at Yangkou Beach in Shouguang City, Shandong Province. Various algorithms were employed for data classification and comparison, with support vector machine (SVM) and neural network algorithms demonstrating superior classification accuracy. The experimental results indicate that the UAV-based hyperspectral imaging system exhibits high imaging quality, minimal distortion, excellent resolution, an expansive camera field of view, a broad detection range, high experimental efficiency, and remarkable capabilities for remote sensing detection.

Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy

Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data, including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection.

WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring

This paper describes a prototype network of automated in situ measurements of hyperspectral water reflectance suitable for satellite validation and water quality monitoring. Radiometric validation of satellite-derived water reflectance is essential to ensure that only reliable data, e.g., for estimating water quality parameters such as chlorophyll a concentration, reach end-users. Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. Two hyperspectral measurement systems, PANTHYR (based on the mature TRIOS/RAMSES radiometer) and HYPSTAR® (a newly designed radiometer), have been integrated here in the WATERHYPERNET network with SI-traceable calibration and characterisation. The systems have common data acquisition protocol, data processing and quality control. The choice of validation site and viewing geometry and installation considerations are described in detail. Three demonstration cases are described: 1. PANTHYR data from two sites are used to validate Sentinel-2/MSI (A&B); 2. HYPSTAR® data at six sites are used to validate Sentinel-3/OLCI (A&B); 3. PANTHYR and HYPSTAR® data in Belgian North Sea waters are used to monitor phytoplankton parameters, including Phaeocystis globosa, over two 5 month periods. Conclusion are drawn regarding the quality of Sentinel-2/MSI and Sentinel-3/OLCI data, including indications where improvements could be made. For example, a positive bias (mean difference) is found for ACOLITE_DSF processing of Sentinel-2 in clear waters (Acqua Alta) and clues are provided on how to improve this processing. The utility of these in situ measurements, even without accompanying hyperspectral satellite data, is demonstrated for phytoplankton monitoring. The future evolution of the WATERHYPERNET network is outlined, including geographical expansion, improvements to hardware reliability and to the measurement method (including uncertainty estimation) and plans for daily distribution of near real-time data.

Spatial-spectral encoding and dictionary optimization in compressive single-pixel hyperspectral imaging based on mutual coherence minimization

A single-pixel detector based hyperspectral system provides an effective way to obtain the spatial-spectral information of target scenes. However, complex spectral dispersion and the substantial number of measurements not only increase the complexity of the system but also decrease the sampling efficiency and the reconstruction accuracy. In this paper, we propose a compressive sensing (CS) theory based single-pixel hyperspectral imaging system. Based on structured illumination, the spatial information is modulated by binary spatial patterns displayed on a liquid crystal on silicon (LCoS), while polarizing elements at specific angles, acting as a serious of filters, modulate the spectral dimension, effectively avoiding spectral dispersion. In terms of sampling efficiency, the application of CS significantly decreases the number of measurements required compared to the Nyquist-Shannon sampling theorem. Besides, to improve the reconstruction accuracy, mutual coherence minimization is employed to optimize the pre-trained dictionary, spatial patterns and filters. Furthermore, a two-step encoding method based on macro-pixel segmentation is proposed to address the issue of low resolution constrained by the size of the dictionary. Compared to the unoptimized system and dictionary, the proposed method achieves more accurate reconstruction results in both spectral and spatial dimensions. This work may provide opportunities for high-resolution single-pixel hyperspectral imaging systems based on CS.

HYPSTAR: a hyperspectral pointable system for terrestrial and aquatic radiometry

Optical Earth observation satellites provide vast amounts of data on a daily basis. The top-of-atmosphere radiance measured by these satellites is usually converted to bottom-of-atmosphere radiance or reflectance which is then used for deriving numerous higher level products used for monitoring environmental conditions, climate change, stock of natural resources, etc. The increase of available remote sensing data impacts decision-making on both regional and global scales, and demands appropriate quality control and validation procedures. A HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR®) has been designed to provide automated, in-situ multiangular reflectance measurements of land and water targets. HYPSTAR-SR covers 380–1020 nm spectral range at 3 nm spectral resolution and is used at water sites. For land sites the HYPSTAR-XR variant is used with the spectral range extended to 1680 nm at 10 nm spectral resolution. The spectroradiometer has multiplexed radiance and irradiance entrances, an internal mechanical shutter, and an integrated imaging camera for capturing snapshots of the targets. The spectroradiometer is mounted on a two-axis pointing system with 360° range of free movement in both axes. The system also incorporates a stable light emitting diode as a light source, used for monitoring the stability of the radiometric calibration during the long-term unattended field deployment. Autonomous operation is managed by a host system which handles data acquisition, storage, and transmission to a central WATERHYPERNET or LANDHYPERNET server according to a pre-programmed schedule. The system is remotely accessible over the internet for configuration changes and software updates. The HYPSTAR systems have been deployed at 10 water and 11 land sites for different periods ranging from a few days to a few years. The data are automatically processed at the central servers by the HYPERNETS processor and the derived radiance, irradiance, and reflectance products with associated measurement uncertainties are distributed at the WATERHYPERNET and LANDHYPERNET data portals.

Research on Luminance Uniformity of Green Micro-LED via Microscopic Hyperspectral System

Research on Luminance Uniformity of Green Micro-LED via Microscopic Hyperspectral System

Design and verification of a highly accurate in-situ hyperspectral radiometric measurement system (HyperNav)

Hyperspectral optical observations of the Earth’s surface oceans from space offer a means to improve our understanding of ocean biology and biogeochemistry. NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, which includes a hyperspectral ocean color instrument (OCI), will provide radiometric observations of surface ocean with near continuous spectral resolution across the near UV to NIR range. Maintaining sufficient accuracy over the lifetime of satellite ocean color missions requires a robust program for system vicarious calibration (SVC) and product validation. The system vicarious calibration process combines satellite sensor data with in-situ radiometric/optical measurements to remove potential biases due to the combined errors from both satellite radiometric sensor calibration and atmospheric correction. As such, high accuracy, high-spectral resolution in-situ radiometric measurements are required to provide a principal source of truth for the satellite-derived products. To meet the requirements, a novel in-situ radiometric system, called HyperNav, has been developed, rigorously characterized and field tested. Key attributes of HyperNav are dual upwelling radiance heads coupled to individual spectrometers, spectral resolution of ∼2.2 nm (full width, half-maximum) across 320–900 nm, integrated shutter systems for dark measurements, and integrated tilt and pressure sensors. The HyperNav operational modes include traditional profiling and surface modes, as well as integration with an autonomous profiling float for unattended deployment, offering a new capability for a network of autonomous platforms to support the long-term needs for hyperspectral ocean color remote sensing observations. This paper describes the HyperNav design, in-situ operational modes, and field verification results.

Optical Design of a Hyperspectral Remote-Sensing System Based on an Image-Slicer Integral Field Unit in the Short-Wave Infrared Band.

Grating-type spectral imaging systems are frequently employed in scenes for high-resolution remote-sensing observations of the Earth. However, the entrance of the grating-type spectral imaging system is a slit or a pinhole. This structure relies on the push broom method, which presents a challenge in capturing spectral information of transiently changing targets. To address this issue, the IFU is used to slice the focal plane of the telescope system, thereby expanding the instantaneous field of view (IFOV) of the grating-type spectral imaging system. The aberrations introduced by the expansion of the single-slice field of view (FOV) of the IFU are corrected, and the conversion of the IFU's FOV from arcseconds to degrees is achieved. The design of a spectral imaging system based on an image-slicer IFU for remote sensing is finally completed. The system has a wavelength range of 1400 nm to 2000 nm, and a spectral resolution of better than 3 nm. Compared with the traditional grating-type spectral imaging system, its IFOV is expanded by a factor of four. And it allows for the capture of complete spectral information of transiently changing targets through a single exposure. The simulation results demonstrate that the system has good performance at each sub-slit, thereby validating the effectiveness and advantages of the proposed system for dynamic target capture in remote sensing.

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens.

Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival. We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures. A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens. A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument. We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

Remote retrieval of dissolved organic carbon in rivers using a hyperspectral drone system

ABSTRACT Rivers act as the principal channels for transporting terrigenous dissolved organic carbon (DOC) to lakes and reservoirs. Satellite remote sensing-based river monitoring is difficult due to the narrow river form and high spatiotemporal heterogeneity of DOC components. The unique advantages of unmanned aerial vehicles (UAVs) facilitate river DOC concentration monitoring. The DOC concentration in 8 major tributaries (average width: 109.62 m) and shoreside of the Lake Chaohu Basin were retrieved via a hyperspectral UAV. The results showed that (1) the DOC concentration was significantly correlated with the water remote sensing reflectance ( R rs ) of 402, 429-438, 440–451 and 458–462 nm in the blue band (r2: 0.11 to 0.13; p<0.05), and 620–621 and 623–693 nm in the red band (r2: 0.12 to 0.20; p<0.05). The water quality parameters chlorophyll-a (Chl-a) and suspended particulate matter (SPM) and environmental parameters wind speed and temperature 3 days delay sampling date, also showed a significant correlation. (2) The random forest regression (RFR) model attained the best performance (r2: 0.64; RMSE: 0.30 mg/L; MAPE: 7.02%). (3) DOC concentration in Lake Chaohu Basin was highest in the northeast (8.19 mg/L), followed by the northwest and west (7.13 mg/L), and it was lowest in the south (6.70 mg/L).

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

An effective adaptive deep learning method combined with a hyperspectral system to identify the soybeans quality from different regions

An effective method that influences classification performance is the extraction of spectral information features. In this study, Global Spatial Spectral Attention Network (GSSA-Net) is designed and integrated with a hyperspectral system to identify soybeans quality across various regions. Initially, a hyperspectral system is utilized to capture spectral information from soybeans in various regions. Subsequently, Global Spatial Spectral Attention (GSSA) is proposed to focus on deep spectral features by calculating attention in multiple subspaces. Ultimately, GSSA-Net is designed to achieve efficient classification of spectral information from various regions. When compared to other state-of-the-art methods for spectral information classification, GSSA-Net demonstrates superior performance with an accuracy of 98.41 %, precision of 98.50 %, and recall of 98.60 %. In summary, the integration of GSSA-Net and the hyperspectral system offers an effective technical approach for identifying soybean quality across different regions.

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System.

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

Development of a visible to 1600 nm hyperspectral imaging rigid-scope system using supercontinuum light and an acousto-optic tunable filter

In this study, we developed a rigid-scope system that can perform hyperspectral imaging (HSI) between visible and 1600 nm wavelengths using a supercontinuum light source and an acousto-optic tunable filter to emit specific wavelengths. The system optical performance was verified, and the classification ability was investigated. Consequently, it was demonstrated that HSI (490–1600 nm) could be performed. In addition, seven different targets could be classified by the neural network with an accuracy of 99.6%, recall of 93.7%, and specificity of 99.1% when the wavelength range of over 1000 nm (OTN) was extracted from HSI data as train data.

Hyperspectral imaging combined with GA-SVM for maize variety identification.

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

Using the automated HYPERNETS hyperspectral system for multi-mission satellite ocean colour validation in the Río de la Plata, accounting for different spatial resolutions

Validation of water reflectance using in situ data is essential to ensure the quality of ocean colour satellite-derived products useful for water quality monitoring, like turbidity and chlorophyll-a concentration. Since December 2021, the HYPERNETS automated hyperspectral system has been collecting data in the optically complex and highly turbid waters of the Río de la Plata, an ideal scenario for testing atmospheric correction algorithms’ performance. The site, located 60 km south of Buenos Aires (Argentina), is described in relation to the water reflectance spectral features and variability using high spatial resolution imagery and a methodology is proposed to objectively select a sensor-specific location of a reference pixel for satellite validation. Six months of data is used to evaluate surface water reflectance operational products from multi-spectral systems like Landsat 8&amp;amp;9/OLI (L89/OLI), Sentinel-2/MSI (S2/MSI) &amp;amp; Sentinel-3/OLCI (S3/OLCI), and PlanetScope SuperDoves (PS/SD), and also non standard products for Aqua/MODIS (Aqua/MODIS) and SNPP&amp;amp;JPSS1/VIIRS (SJ/VIIRS) missions. Moreover, the standard surface water reflectance product from the hyperspectral PRISMA mission could also be evaluated. The matchups show general good results when in situ measurements are compared to L2 standard products of high spatial resolution sensors that use land-based atmospheric correction approach, if sun glint contamination is avoided. Low mean relative percentage difference was found for S2/MSI (2.45%) and L89/OLI (−3.52%), but higher for PS/SD (30.7%). In turn, S3/OLCI medium resolution also showed low mean relative differences (2.31%), while SJ/VIIRS and Aqua/MODIS showed larger and negative differences (−16.35 for SJ/VIIRS and −35.6% for Aqua/MODIS) which showed a clear increase towards the shortest blue bands. The results show the great potential of the HYPERNETS automated system to provide high quality and quantity of data for validation of satellite data at all visible and near infrared (VNIR, 400–900 nm) wavelengths in a multi-mission perspective.

Hyperspectral image dynamic range reconstruction using deep neural network-based denoising methods

Hyperspectral (HS) measurement is among the most useful tools in agriculture for early disease detection. However, the cost of HS cameras that can perform the desired detection tasks is prohibitive-typically fifty thousand to hundreds of thousands of dollars. In a previous study at the Agricultural Research Organization’s Volcani Institute (Israel), a low-cost, high-performing HS system was developed which included a point spectrometer and optical components. Its main disadvantage was long shooting time for each image. Shooting time strongly depends on the predetermined integration time of the point spectrometer. While essential for performing monitoring tasks in a reasonable time, shortening integration time from a typical value in the range of 200 ms to the 10 ms range results in deterioration of the dynamic range of the captured scene. In this work, we suggest correcting this by learning the transformation from data measured with short integration time to that measured with long integration time. Reduction of the dynamic range and consequent low SNR were successfully overcome using three developed deep neural networks models based on a denoising auto-encoder, DnCNN and LambdaNetworks architectures as a backbone. The best model was based on DnCNN using a combined loss function of ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _{2}$$\\end{document} and Kullback–Leibler divergence on images with 20 consecutive channels. The full spectrum of the model achieved a mean PSNR of 30.61 and mean SSIM of 0.9, showing total improvement relatively to the 10 ms measurements’ mean PSNR and mean SSIM values by 60.43% and 94.51%, respectively.

Validation of satellite water products based on HYPERNETS in situ data using a Match-up Database (MDB) file structure

A Match-up Database (MDB) file structure and tools were developed to ease the validation analysis of satellite water products and to improve the exchange and processing of match-up data from different sites, missions and atmospheric correction processors. In situ remote sensing reflectance (Rrs) measurements were available from the HYPSTAR® (HYperspectral Pointable System for Terrestrial and Aquatic Radiometry), a new automated hyperspectral radiometer. An MDB file is a NetCDF file containing all the potential match-ups between satellite and in situ data on a specific site and within a given time window. These files are generated and manipulated with three modules developed in Python to implement the validation protocols: extract satellite data, associate each extract with co-located in situ radiometry data, and then perform the validation analysis. This work provides details on the implementation of the open-source MDB file structure and tools. The approach is demonstrated by a multi-site matchup comparison based on satellite data from the Sentinel-2 MSI and Sentinel-3 OLCI sensors, and HYPSTAR® data acquired over six water sites with diverse optical regimes from February 2021 to March 2023.The analysis of Sentinel-3 OLCI matchups across the six sites shows consistency with previous comparisons based on AERONET-OC data over extended reflectance range. We evaluated Sentinel-2 MSI reflectance data corrected with two atmospheric correction processors (ACOLITE and C2RCC) over four sites with clear to highly turbid waters. Results showed that the performance of the processors depends on the optical regime of the sites. Overall, we proved the suitability of the open-source MDB-based approach to implement validation protocols and generate automated matchup analyses for different missions, processors and sites.