scholarly journals The Pan-and-Tilt Hyperspectral Radiometer System (PANTHYR) for Autonomous Satellite Validation Measurements—Prototype Design and Testing

2019 ◽  
Vol 11 (11) ◽  
pp. 1360 ◽  
Author(s):  
Dieter Vansteenwegen ◽  
Kevin Ruddick ◽  
André Cattrijsse ◽  
Quinten Vanhellemont ◽  
Matthew Beck
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Lessons Learned ◽  
Offshore Platforms ◽  
Satellite Mission ◽  
Data Archiving ◽  
Future Evolution ◽  
Software Upgrades ◽  
Autonomous Operation ◽  
Commercial Off The Shelf

This paper describes a system, named “pan-and-tilt hyperspectral radiometer system” (PANTHYR) that is designed for autonomous measurement of hyperspectral water reflectance. The system is suitable for deployment in diverse locations (including offshore platforms) for the validation of water reflectance derived from any satellite mission with visible and/or near-infrared spectral bands (400–900 nm). Key user requirements include reliable autonomous operation at remote sites without grid power or cabled internet and only limited maintenance (1–2 times per year), flexible zenith and azimuth pointing, modularity to adapt to future evolution of components and different sites (power, data transmission, and mounting possibilities), and moderate hardware acquisition cost. PANTHYR consists of two commercial off-the-shelf (COTS) hyperspectral radiometers, mounted on a COTS pan-and-tilt pointing system, controlled by a single-board-computer and associated custom-designed electronics which provide power, pointing instructions, and data archiving and transmission. The variable zenith pointing improves protection of sensors which are parked downward when not measuring, and it allows for use of a single radiance sensor for both sky and water viewing. The latter gives cost reduction for radiometer purchase, as well as reduction of uncertainties associated with radiometer spectral and radiometric differences for comparable two-radiance-sensor systems. The system is designed so that hardware and software upgrades or changes are easy to implement. In this paper, the system design requirements and choices are described, including details of the electronics, hardware, and software. A prototype test on the Acqua Alta Oceanographic Tower (near Venice, Italy) is described, including comparison of the PANTHYR system data with two other established systems: the multispectral autonomous AERONET-OC data and a manually deployed three-sensor hyperspectral system. The test established that high-quality hyperspectral data for water reflectance can be acquired autonomously with this system. Lessons learned from the prototype testing are described, and the future perspectives for the hardware and software development are outlined.

PRISMA hyperspectral satellite mission: first data on snow in the Alps

2020 ◽  
Author(s):  
Biagio Di Mauro ◽  
Roberto Garzonio ◽  
Gabriele Bramati ◽  
Sergio Cogliati ◽  
Edoardo Cremonese ◽  
...  
Keyword(s):  
Grain Size ◽  
Near Infrared ◽  
Snow Water Equivalent ◽  
Satellite Observation ◽  
Hyperspectral Data ◽  
European Alps ◽  
Spectral Evolution ◽  
Satellite Mission ◽  
Space Agency ◽  
Italian Space Agency

<p>On the 22<sup>nd</sup> of March 2019, PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission has been launched by the Italian Space Agency (ASI). Since then, the spacecraft has been collecting on demand hyperspectral data of the Earth surface. The imaging spectrometer features 239 bands covering the visible, near infrared and shortwave infrared wavelengths (400-2500 nm) with a spectral resolution <12nm. PRISMA acquires hyperspectral images with a spatial resolution of 30m and a swath of 30 km.</p><p>The satellite mission is still in the initial commissioning phase. During this period, the acquisition of field spectroscopy data contemporary to satellite observation is fundamental. With the aim of calibrating and validating PRISMA observations on snow fields, we organized field campaigns at a high altitude (2160 m) experimental site (Torgnon, Aosta Valley) in the European Alps. During these campaigns, we measured spectral reflectance of snow with a Spectral Evolution spectrometer (350-2500 nm), snow grain size, and snow density. Among different instruments operating at the site (e.g. net radiometer, webcam, sensors for snow depth, snow water equivalent, snow surface temperature etc.), we recently installed an unattended spectrometer acquiring continuous measurements of snow reflectance. This instrument covers part of the visible and near infrared spectral range (400-900 nm) and it was used to analyze the daily evolution of snow reflectance during the snow season.</p><p>In this contribution, we present a preliminary comparison between field and satellite hyperspectral reflectance data of snow. This comparison is fundamental for the future development of algorithms for the estimation of snow physical variables (snow grain size, snow albedo, and concentration of impurities) from satellite hyperspectral data.</p>

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 341
Author(s):  
Pauliina Salmi ◽  
Matti A. Eskelinen ◽  
Matti T. Leppänen ◽  
Ilkka Pölönen
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Strong Correlations ◽  
Algae Biomass ◽  
Chlorophyll A Concentration ◽  
Cost Efficient ◽  
Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

Chemical Deliquification of Unconventional Gas Wells

2014 ◽  
Author(s):  
K.. Francis-LaCroix ◽  
D.. Seetaram
Keyword(s):  
Natural Gas ◽  
Best Practice ◽  
Oil And Gas ◽  
Large Deviation ◽  
Cost Effective ◽  
Gas Production ◽  
Lessons Learned ◽  
Offshore Platforms ◽  
Gas Wells ◽  
Current Production

Abstract Trinidad and Tobago offshore platforms have been producing oil and natural gas for over a century. Current production of over 1500 Bcf of natural gas per year (Administration, 2013) is due to extensive reserves in oil and gas. More than eighteen of these wells are high-producing wells, producing in excess of 150 MMcf per day. Due to their large production rates, these wells utilize unconventionally large tubulars 5- and 7-in. Furthermore, as is inherent with producing gas, there are many challenges with the production. One major challenge occurs when wells become liquid loaded. As gas wells age, they produce more liquids, namely brine and condensate. Depending on flow conditions, the produced liquids can accumulate and induce a hydrostatic head pressure that is too high to be overcome by the flowing gas rates. Applying surfactants that generate foam can facilitate the unloading of these wells and restore gas production. Although the foaming process is very cost effective, its application to high-producing gas wells in Trinidad has always been problematic for the following reasons: Some of these producers are horizontal wells, or wells with large deviation angles.They were completed without pre-installed capillary strings.They are completed with large tubing diameters (5.75 in., 7 in.). Recognizing that the above three factors posed challenges to successful foam applications, major emphasis and research was directed toward this endeavor to realize the buried revenue, i.e., the recovery of the well's potential to produce natural gas. This research can also lead to the application of learnings from the first success to develop treatment for additional wells, which translates to a revenue boost to the client and the Trinidad economy. Successful treatments can also be used as correlations to establish an industry best practice for the treatment of similarly completed wells. This paper will highlight the successes realized from the treatment of three wells. It will also highlight the anomalies encountered during the treatment process, as well as the lessons learned from this treatment.

Research on CNN Coal and Rock Recognition Method Based on Hyperspectral Data

2021 ◽  
Author(s):  
Jianjian Yang ◽  
Boshen Chang ◽  
Yuchen Zhang ◽  
Wenjie Luo ◽  
Miao Wu
Keyword(s):  
Near Infrared ◽  
Dropout Rate ◽  
Hyperspectral Data ◽  
Standard Normal ◽  
Infrared Spectrometer ◽  
Pros And Cons ◽  
Mining Face ◽  
The One ◽  
Near Infrared Spectrometer ◽  
Spectrum Data

Abstract Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing links, this article proposes a CNN coal and rock identification method based on hyperspectral data. First, collect coal and rock spectrum data by a near-infrared spectrometer, and then use four methods such as first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing to filter the 120 sets of collected data. The coal and rock reflectance spectrum data is preprocessed to enhance the intensity of spectral reflectance and absorption characteristics, and effectively remove the spectral curve noise generated by instrument performance and environmental factors.Construct a CNN model, judge the pros and cons of the model by comparing the accuracy of the three parameter combinations, select the most appropriate learning rate, the number of feature extraction layers, and the dropout rate, and generate the best CNN classifier for hyperspectral data. Rock recognition. Experiments show that the recognition accuracy of the one-dimensional convolutional neural network model proposed in this paper reaches 94.6%, which is higher than BP (57%), SVM (72%) and DBN (86%). Verify the advantages and effectiveness of the method proposed in this article.

scholarly journals Using Visual Ozone Damage Scores and Spectroscopy to Quantify Soybean Responses to Background Ozone

2019 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Nichole Gosselin ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Jack Fishman ◽  
Kelley Belina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Pearson Correlation ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Biophysical Properties ◽  
Yield Data ◽  
Indicator Plants ◽  
Snap Beans ◽  
Visual Damage ◽  
Ozone Stress

Remotely-sensed identification of ozone stress in crops can allow for selection of ozone resistant genotypes, improving yields. This is critical as population, food demand, and background tropospheric ozone are projected to increase over the next several decades. Visual scores of common ozone damage have been used to identify ozone-stress in bio-indicator plants. This paper evaluates the use of a visual scoring metric of ozone damage applied to soybeans. The scoring of the leaves is then combined with hyperspectral data to identify spectral indices specific to ozone damage. Two genotypes of soybean, Dwight and Pana, that have shown different sensitivities to ozone, were grown and visually scored for ozone-specific damage on multiple dates throughout the growing season. Leaf reflectance, foliar biophysical properties, and yield data were collected. Additionally, ozone bio-indicator plants, snap beans, and common milkweed, were investigated with visual scores and hyperspectral leaf data for comparison. The normalized difference spectral index (NDSI) was used to identify the significant bands in the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) that best correlated with visual damage score when used in the index. Results were then compared to multiple well-established indices. Indices were also evaluated for correlation with seed and pod weight. The ozone damage scoring metric for soybeans evaluated in August had a coefficient of determination of 0.60 with end-of-season pod weight and a Pearson correlation coefficient greater than 0.6 for photosynthetic rate, stomatal conductance, and transpiration. NDSI [R558, R563] correlated best with visual scores of ozone damage in soybeans when evaluating data from all observation dates. These wavelengths were similar to those identified as most sensitive to visual damage in August when used in NDSI (560 nm, 563 nm). NDSI [R560, R563] in August had the highest coefficient of determination for individual pod weight (R2 = 0.64) and seed weight (R2 = 0.54) when compared against 21 well-established indices used for identification of pigment or photosynthetic stress in plants. When evaluating use of spectral bands in NDSI, longer wavelengths in SWIR were identified as more sensitive to ozone visual damage. Trends in the bands and biophysical properties of the soybeans combined with evaluation of ozone data indicate likely timing of significant ozone damage as after late-July for this season. This work has implications for better spectral detection of ozone stress in crops and could help with efforts to identify ozone tolerant varieties to increase future yield.

scholarly journals Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Zhang ◽  
Biyao Wang ◽  
Pengtao Yan ◽  
Kunlun Wang ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Injection ◽  
Principal Component ◽  
Local Extremum ◽  
Hyperspectral Data ◽  
Identification Accuracy ◽  
Hyperspectral Images ◽  
Nondestructive Identification ◽  
Som Network ◽  
Weighting Coefficients

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%.

scholarly journals Space Astrometry JASMINE

2007 ◽  
Vol 3 (S248) ◽  
pp. 296-297 ◽  
Author(s):  
T. Yano ◽  
N. Gouda ◽  
Y. Kobayashi ◽  
Y. Yamada ◽  
T. Tsujimoto ◽  
...  
Keyword(s):  
Near Infrared ◽  
Focal Length ◽  
Field Of View ◽  
Galactic Bulge ◽  
Satellite Mission ◽  
Fully Depleted ◽  
Thick Substrate ◽  
Trigonometric Parallaxes ◽  
Space Astrometry ◽  
Back Illuminated

AbstractJASMINE is the acronym of the Japan Astrometry Satellite Mission for INfrared (z-band: 0.9 micron) Exploration, and is planned to be launched around 2017. The main objective of JASMINE is to study the fundamental structure and evolution of the Milky Way bulge components. In order to accomplish these objectives, JASMINE will measure trigonometric parallaxes, positions and proper motions of about ten million stars in the Galactic bulge with a precision of 10 microarcsec at z = 14mag.The primary mirror for the telescope has a diameter of 75cm with a focal length of 22.5m. The back-illuminated CCD is fabricated on a 300 micron thick substrate which is fully depleted. These thick devices have extended near infrared response. The size of the detector for z-band is 3cm×3cm with 2048×2048 pixels. The size of the field of view is about 0.6deg×0.6deg by using 64 detectors on the focal plane. The telescope is designed to have only one field of view, which is different from the designs of other astrometric satellites. JASMINE will observe overlapping fields without gaps to survey a total area of about 20deg×10 deg around the Galactic bulge. Accordingly we make a “large frame” of 20deg×10 deg by linking the small frames using stars in overlapping regions. JASMINE will observe the Galactic bulge repeatedly during the mission life of about 5 years.

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