Showing papers on "VNIR published in 2017"

Plant Disease Detection Using Hyperspectral Imaging

Abstract: Precision agriculture has enabled significant progress in improving yield outcomes for farmers. Recent progress in sensing and perception promises to further enhance the use of precision agriculture by allowing the detection of plant diseases and pests. When coupled with robotics methods for spatial localisation, early detection of plant diseases will al- low farmers to respond in a timely and localised manner to dis- ease outbreaks and limit crop damage. This paper proposes the use of hyperspectral imaging (VNIR and SWIR) and machine learning techniques for the detection of the Tomato Spotted Wilt Virus (TSWV) in capsicum plants. Discriminatory features are extracted using the full spectrum, a variety of vegetation indices, and probabilistic topic models. These features are used to train classifiers for discriminating between leaves obtained from healthy and inoculated plants. The results show excellent discrimination based on the full spectrum and comparable results based on data-driven probabilistic topic models and the domain vegetation indices. Additionally our results show increasing classification performance as the dimensionality of the features increase.

Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS

Abstract: This study describes the development of a small hyperspectral Unmanned Aircraft System (HyUAS) for measuring Visible and Near-Infrared (VNIR) surface reflectance and sun-induced fluorescence, co-registered with high-resolution RGB imagery, to support field spectroscopy surveys and calibration and validation of remote sensing products. The system, namely HyUAS, is based on a multirotor platform equipped with a cost-effective payload composed of a VNIR non-imaging spectrometer and an RGB camera. The spectrometer is connected to a custom entrance optics receptor developed to tune the instrument field-of-view and to obtain systematic measurements of instrument dark-current. The geometric, radiometric and spectral characteristics of the instruments were characterized and calibrated through dedicated laboratory tests. The overall accuracy of HyUAS data was evaluated during a flight campaign in which surface reflectance was compared with ground-based reference measurements. HyUAS data were used to estimate spectral indices and far-red fluorescence for different land covers. RGB images were processed as a high-resolution 3D surface model using structure from motion algorithms. The spectral measurements were accurately geo-located and projected on the digital surface model. The overall results show that: (i) rigorous calibration enabled radiance and reflectance spectra from HyUAS with RRMSE < 10% compared with ground measurements; (ii) the low-flying UAS setup allows retrieving fluorescence in absolute units; (iii) the accurate geo-location of spectra on the digital surface model greatly improves the overall interpretation of reflectance and fluorescence data. In general, the HyUAS was demonstrated to be a reliable system for supporting high-resolution field spectroscopy surveys allowing one to collect systematic measurements at very detailed spatial resolution with a valuable potential for vegetation monitoring studies. Furthermore, it can be considered a useful tool for collecting spatially-distributed observations of reflectance and fluorescence that can be further used for calibration and validation activities of airborne and satellite optical images in the context of the upcoming FLEX mission and the VNIR spectral bands of optical Earth observation missions (i.e., Landsat, Sentinel-2 and Sentinel-3).

Assessment of WorldView-3 Data for Lithological Mapping

Abstract: The WorldView-3 (WV-3) satellite is a new sensor with high spectral resolution, which equips eight multispectral bands in the visible and near-infrared (VNIR) and additional eight bands in the shortwave infrared (SWIR). In order to meet the requirements of large-scale geological mapping, this paper assessed WV-3 data for lithological mapping in comparison with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Operational Land Imager (OLI/Landsat-8) data. The study area is located in the Pobei area of the Xinjiang Uygur Autonomous Region, where bedrock outcrops are widely distributed. The whole experiment was divided into six steps: data pre-processing, visual interpretation of various lithological units, samples procedure, lithological mapping by a support vector machine algorithm (SVM), accuracy evaluation, and assessment. The results showed that the classification accuracy of WV-3 data was 87%, which kept 17% higher than that of ASTER data, 14% higher than that of OLI/Landsat-8 data, indicated that WV-3 data contained more diagnostic absorption features mainly thanks to its SWIR bands, and benefited by its high spatial resolution, as well. However, it also confirmed that there were some considerable flaws, such as the confusing identification of biotite-quartz schist. Overall, the WV-3 data is still the most promising data for geological applications currently.

Organic carbon prediction in soil cores using VNIR and MIR techniques in an alpine landscape

Abstract: Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic carbon (SOC). We examined 24 soil cores (0–100 cm) from the Sygera Mountains on the Qinghai–Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM). The SVM models performed better with three predictors, with the ratio of performance to inter-quartile distance (RPIQ) and R 2 values typically exceeding 1.74 and 0.73, respectively. The SVM using the DRS technique indicated accurate predictive results of SOC in each core. The RPIQ values of the shrub meadow, forest and total dataset prediction using air-dried ground VNIR were 1.97, 2.68 and 1.99, respectively; the values using field-moist intact VNIR were 1.95, 2.07 and 1.76 and those using air-dried ground MIR were 1.78, 1.96 and 1.74, respectively. We conclude that the DRS technique is an efficient and rapid method for SOC prediction and has the potential for dynamic monitoring of SOC stock density on the Qinghai–Tibet Plateau.

Extracting mineral alteration information using WorldView-3 data

Abstract: The multispectral commercial satellite-WorldView-3 (WV-3) has the highest spatial, spectral and radiation resolutions among the satellites currently and can generate good data in the shortwave infrared (SWIR). The study area is located in the Pobei area of Xinjiang Uygur Autonomous Region, which is rich in mineral resources. The spectral analyses of some typical altered hydroxy-bearing, iron-bearing, and carbonate-bearing minerals could establish several Principal Component Analysis (PCA) models and mineral indices, using the visible and near infrared (VNIR) and the shortwave infrared (SWIR) subsystems of WV-3 data. In addition, the Spectral Angle Method and the spectrum index tool of ENVI software were used to extract the relevant alteration information. The WV-3 data is well suited for identifying hydroxy-bearing alteration with rich SWIR bands which distinguish Al-OH-bearing from Mg-OH-bearing alteration. Hence, this study provides a basis for the prediction of mineral resources in the Pobei area and sets the foundation for WV-3 data to be used as an important tool in extracting alteration information and prospecting practices.

Sharpening the VNIR and SWIR bands of Sentinel-2A imagery through modified selected and synthesized band schemes

Abstract: In this work, the bands of a Sentinel-2A image with spatial resolutions of 20 m and 60 m are sharpened to a spatial resolution of 10 m to obtain visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral bands with a spatial resolution of 10 m. In particular, we propose a two-step sharpening algorithm for Sentinel-2A imagery based on modified, selected, and synthesized band schemes using layer-stacked bands to sharpen Sentinel-2A images. The modified selected and synthesized band schemes proposed in this study extend the existing band schemes for sharpening Sentinel-2A images with spatial resolutions of 20 m and 60 m to improve the pan-sharpening accuracy by changing the combinations of bands used for multiple linear regression analysis through band-layer stacking. The proposed algorithms are applied to the pan-sharpening algorithm based on component substitution (CS) and a multiresolution analysis (MRA), and our results are then compared to the sharpening results when using sharpening algorithms based on existing band schemes. The experimental results show that the sharpening results from the proposed algorithm are improved in terms of the spatial and spectral properties when compared to existing methods. However, the results of the sharpening algorithm when applied to our modified band schemes show differing tendencies. With the modified, selected band scheme, the sharpening result when applying the CS-based algorithm is higher than the result when applying the MRA-based algorithm. However, the quality of the sharpening results when using the MRA-based algorithm with the modified synthesized band scheme is higher than that when using the CS-based algorithm.

Visible-Near Infrared Reflectance Spectroscopy for Rapid Characterization of Salt-Affected Soil in the Indo-Gangetic Plains of Haryana, India

Abstract: Management of salt-affected soils is a challenging task in the input intensive rice-wheat cropping zone of the Indo-Gangetic plains (IGP). Timely detection of salt-affected areas and assessment of the degree of severity are vital in order to narrow down the potential gap in yield. Conventional laboratory techniques of saturation extract electrical conductivity (ECe) and sodium adsorption ration (SAR) for soil salinity assessment are time-consuming and labour intensive; the VNIR (visible-near infrared) reflectance spectroscopy technique provides ample information on salinity and its attributes in an efficient and cost-effective way. This study aims to develop robust soil reflectance spectral models for rapid assessment of soil salinity in the salt affected areas of the IGP region of Haryana using VNIR reflectance spectroscopy. The results indicated that the spectral region between 1390 and 2400 nm was highly sensitive to measure changes in salinity. The developed hyperspectral models explained more than 80 % variability in ECe, and other salinity related attributes (saturated extract Na+, Ca2+ + Mg2+, Cl− and SAR) in the validation datasets. With the increasing availability of data from hyperspectral sensors in near future, the study will be very useful in real time monitoring of soils in the spatio-temporal context; enabling the farmers of IGP area to deal with salt degradation more effectively and efficiently.

High Resolution Temperature Measurement of Liquid Stainless Steel Using Hyperspectral Imaging.

Abstract: A contactless temperature measurement system is presented based on a hyperspectral line camera that captures the spectra in the visible and near infrared (VNIR) region of a large set of closely spaced points. The measured spectra are used in a nonlinear least squares optimization routine to calculate a one-dimensional temperature profile with high spatial resolution. Measurements of a liquid melt pool of AISI 316L stainless steel show that the system is able to determine the absolute temperatures with an accuracy of 10%. The measurements are made with a spatial resolution of 12 µm/pixel, justifying its use in applications where high temperature measurements with high spatial detail are desired, such as in the laser material processing and additive manufacturing fields.

Early radiometric calibration performances of GOES-16 advanced baseline imager

Abstract: GOES-16, which was launched on 19 November 2017, is the first of the next generation of geostationary weather satellites of NOAA. The Advanced Baseline Imager (ABI) is the primary instrument and mission critical payload onboard imaging the Earth with 16 different spectral bands covering 6 visible/near-infrared (VNIR) bands and 10 infrared (IR) bands. Although the GOES-16 ABI data are currently experimental and undergoing testing, in this study we focus on reporting some preliminary assessment results of the ABI radiometric calibration performance during the post-launch test (PLT) and post-launch product tests (PLPT) period. Our results show that the ABI IR full-disk (FD) images mean brightness temperature (Tb) bias with respect to S-NPP/CrIS and Metop-B/IASI of less than 0.3K. Diurnal variation is very small with a jump of less than 0.15K occurring twice a day around satellite local noon and midnight. The ABI VNIR radiometric calibration has a mean reflectance difference to SNPP/VIIRS of less than 5% for all the 6 VNIR bands except for B02 (0.64µm), which was about 8% brighter than corresponding VIIRS data during the PLT period. It may be noted that calibration of the VNIR bands experienced instabilities associated with ground system (GS) software patch testing and data receiving site failover testing, which can be reflected with the time-series monitoring from different earth and space-based invariant targets. Validations and investigations are still ongoing to improve the ABI imagery and data quality.

Translational Imaging Spectroscopy for Proximal Sensing.

Abstract: Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a precise pre-processing of hyperspectral data cubes to retrieve at-surface reflectance in order to conduct spectral feature-based comparison of unknown sample spectra to known library spectra. A new pre-processing chain called GeoMAP-Trans for at-surface reflectance retrieval is proposed here as an analogue to other algorithms published by the team of authors. It consists of a radiometric, a geometric and a spectral module. Each module consists of several processing steps that are described in detail. The processing chain was adapted to the broadly used HySPEX VNIR/SWIR imaging spectrometer system and tested using geological mineral samples. The performance was subjectively and objectively evaluated using standard artificial image quality metrics and comparative measurements of mineral and Lambertian diffuser standards with standard field and laboratory spectrometers. The proposed algorithm provides highly qualitative results, offers broad applicability through its generic design and might be the first one of its kind to be published. A high radiometric accuracy is achieved by the incorporation of the Reduction of Miscalibration Effects (ROME) framework. The geometric accuracy is higher than 1 μpixel. The critical spectral accuracy was relatively estimated by comparing spectra of standard field spectrometers to those from HySPEX for a Lambertian diffuser. The achieved spectral accuracy is better than 0.02% for the full spectrum and better than 98% for the absorption features. It was empirically shown that point and imaging spectrometers provide different results for non-Lambertian samples due to their different sensing principles, adjacency scattering impacts on the signal and anisotropic surface reflection properties.

Correlating species and spectral diversities using hyperspectral remote sensing in early-successional fields.

Abstract: Advances in remote sensing technology can help estimate biodiversity at large spatial extents. To assess whether we could use hyperspectral visible near-infrared (VNIR) spectra to estimate species diversity, we examined the correlations between species diversity and spectral diversity in early-successional abandoned agricultural fields in the Ridge and Valley ecoregion of north-central Virginia at the Blandy Experimental Farm. We established plant community plots and collected vegetation surveys and ground-level hyperspectral data from 350 to 1,025 nm wavelengths. We related spectral diversity (standard deviations across spectra) with species diversity (Shannon-Weiner index) and evaluated whether these correlations differed among spectral regions throughout the visible and near-infrared wavelength regions, and across different spectral transformation techniques. We found positive correlations in the visible regions using band depth data, positive correlations in the near-infrared region using first derivatives of spectra, and weak to no correlations in the red-edge region using either of the two spectral transformation techniques. To investigate the role of pigment variability in these correlations, we estimated chlorophyll, carotenoid, and anthocyanin concentrations of five dominant species in the plots using spectral vegetation indices. Although interspecific variability in pigment levels exceeded intraspecific variability, chlorophyll was more varied within species than carotenoids and anthocyanins, contributing to the lack of correlation between species diversity and spectral diversity in the red-edge region. Interspecific differences in pigment levels, however, made it possible to differentiate these species remotely, contributing to the species-spectral diversity correlations. VNIR spectra can be used to estimate species diversity, but the relationships depend on the spectral region examined and the spectral transformation technique used.

Hyperspectral Imaging and Partial Least Square Discriminant Analysis for Geographical Origin Discrimination of White Rice

Abstract: Purpose: This study aims to propose a method for fast geographical origin discrimination between domestic and imported rice using a visible/near-infrared (VNIR) hyperspectral imaging technique. Methods: Hyperspectral reflectance images of South Korean and Chinese rice samples were obtained in the range of 400 nm to 1000 nm. Partial least square discriminant analysis (PLS-DA) models were developed and applied to the acquired images to determine the geographical origin of the rice samples. Results: The optimal pixel dimensions and spectral pretreatment conditions for the hyperspectral images were identified to improve the discrimination accuracy. The results revealed that the highest accuracy was achieved when the hyperspectral image’s pixel dimension was 3.0 mm × 3.0 mm. Furthermore, the geographical origin discrimination models achieved a discrimination accuracy of over 99.99% upon application of a first-order derivative, second-order derivative, maximum normalization, or baseline pretreatment. Conclusions: The results demonstrated that the VNIR hyperspectral imaging technique can be used to discriminate geographical origins of rice.

JPSS-1VIIRS Prelaunch Polarization Testing and Performance

Abstract: The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft, with launch dates of October 2011 and late 2016, respectively, have polarization sensitivity, which affects the at-aperture radiometric calibration. This polarization sensitivity is caused by optics within VIIRS having different reflectance and transmission values as a function of at-aperture photon electric field orientation and is spectrally, spatially, and scan angle dependent. Characterization of the instrument’s polarization sensitivity for each visible near-infrared (VNIR) band and detector was performed prelaunch at multiple cross-track scan angles. The resultant characterization parameters are VIIRS polarization amplitude and phase that enable the at-aperture radiance to be adjusted based on its polarization characteristics. The sensor requirements are that the polarization amplitude for scan angles within ±45° of nadir be below 2.5%–3% depending on the band and have an uncertainty in both amplitude and phase of less than 0.5%. The S-NPP VIIRS passed these requirements with band M1 (412 nm) having the smallest margin (~8%). Modification to the VNIR bandpass filter designs on JPSS-1 was performed to reduce out-of-band response leaks observed prelaunch on S-NPP. An unintended consequence of the spectral bandpass modification was an increase in the polarization sensitivity by roughly a factor of 2 for some VNIR bands. The degree to which JPSS-1 VIIRS polarization sensitivity characterization results exceed the sensor specifications and comparisons with S-NPP will be discussed.

Visible, Very Near IR and Short Wave IR Hyperspectral Drone Imaging System for Agriculture and Natural Water Applications

Abstract: . The accurate determination of the quality parameters of crops requires a spectral range from 400 nm to 2500 nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15 kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high (~ 50 k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Nasila et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded DroneKnowledge – Towards knowledge based export of small UAS remote sensing technology project.

Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka, Southern India using hyperspectral remote sensing data

Abstract: Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bendi...

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Abstract: The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank–Kennard–Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

Downscaling of Land Surface Temperature Using Airborne High-Resolution Data: A Case Study on Aprilia, Italy

Abstract: A regression-based downscaling of land surface temperature was developed over the heterogeneous urban area of Aprilia, Central Italy, using high resolution (HR) airborne data. Airborne sensors provided thermal and visible–near infrared (VNIR) measurements at 2-m pixel size. Coarse resolution images at 40, 30, and 20 m, upscaled by aggregation from the native airborne data, were sharpened to the finer resolution of 2 m. The main core of the downscaling method is the use of the spectral mixture analysis (SMA) to derive fractional pixel composition as predictors of the regression scheme. The HR VNIR data allow choosing detailed land cover types in the application of SMA, such as bright/dark roofs, and the benefit of this detailed selection is proved. The estimation error of the custom technique improves of about 10%–15% with respect to a classical regression downscaling.

A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing

Abstract: This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1-5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2-12.5 µm (instrument NEDT 0.05 K-0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0-10.25 µm and 10.25-12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1-3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.