scholarly journals Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

2021 ◽  
Vol 13 (9) ◽  
pp. 1697
Author(s):  
Alexander Jenal ◽  
Hubert Hüging ◽  
Hella Ellen Ahrends ◽  
Andreas Bolten ◽  
Jens Bongartz ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Near Infrared ◽  
Imaging System ◽  
N Uptake ◽  
Ground Truth ◽  
Short Wave ◽  
Camera System ◽  
Ground Truth Data ◽  
Single Observation ◽  
Wheat Field

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.

scholarly journals Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring

2020 ◽  
Vol 88 (6) ◽  
pp. 493-507
Author(s):  
Alexander Jenal ◽  
Ulrike Lussem ◽  
Andreas Bolten ◽  
Martin Leon Gnyp ◽  
Jürgen Schellberg ◽  
...  
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Short Wave ◽  
Data Sets ◽  
Camera System ◽  
Ground Truth Data ◽  
Calibration Methods

AbstractRemote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.

scholarly journals CHANGING THE PRODUCTION PIPELINE – USE OF OBLIQUE AERIAL CAMERAS FOR MAPPING PURPOSES

2016 ◽  
Vol XLI-B4 ◽  
pp. 631-637
Author(s):  
K. Moe ◽  
I. Toschi ◽  
D. Poli ◽  
F. Lago ◽  
C. Schreiner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Reference Data ◽  
Ground Truth ◽  
Building Extraction ◽  
Camera System ◽  
Ground Truth Data ◽  
Production Pipeline ◽  
Dense Image ◽  
Oblique Images

This paper discusses the potential of current photogrammetric multi-head oblique cameras, such as UltraCam Osprey, to improve the efficiency of standard photogrammetric methods for surveying applications like inventory surveys and topographic mapping for public administrations or private customers. <br><br> In 2015, Terra Messflug (TM), a subsidiary of Vermessung AVT ZT GmbH (Imst, Austria), has flown a number of urban areas in Austria, Czech Republic and Hungary with an UltraCam Osprey Prime multi-head camera system from Vexcel Imaging. In collaboration with FBK Trento (Italy), the data acquired at Imst (a small town in Tyrol, Austria) were analysed and processed to extract precise 3D topographic information. The Imst block comprises 780 images and covers an area of approx. 4.5 km by 1.5 km. Ground truth data is provided in the form of 6 GCPs and several check points surveyed with RTK GNSS. Besides, 3D building data obtained by photogrammetric stereo plotting from a 5 cm nadir flight and a LiDAR point cloud with 10 to 20 measurements per m² are available as reference data or for comparison. The photogrammetric workflow, from flight planning to Dense Image Matching (DIM) and 3D building extraction, is described together with the achieved accuracy. For each step, the differences and innovation with respect to standard photogrammetric procedures based on nadir images are shown, including high overlaps, improved vertical accuracy, and visibility of areas masked in the standard vertical views. Finally the advantages of using oblique images for inventory surveys are demonstrated.

scholarly journals Predictive tracking with improved motion models for optical belt sorting

2020 ◽  
Vol 68 (4) ◽  
pp. 239-255
Author(s):  
Florian Pfaff ◽  
Christoph Pieper ◽  
Georg Maier ◽  
Benjamin Noack ◽  
Robin Gruna ◽  
...  
Keyword(s):  
Ground Truth ◽  
Velocity Model ◽  
Conveyor Belt ◽  
Separation Mechanism ◽  
Multitarget Tracking ◽  
Bulk Materials ◽  
Ground Truth Data ◽  
Single Observation ◽  
Line Scan ◽  
Motion Models

AbstractOptical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type.

scholarly journals Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 422 ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann ◽  
Christopher S. Lapszynski ◽  
Anna Christina Tyler ◽  
Sarah Goldsmith
Keyword(s):  
Moisture Content ◽  
Bulk Density ◽  
Filling Factor ◽  
Imaging System ◽  
Hyperspectral Imagery ◽  
Ground Truth ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ground Truth Data ◽  
The Common

This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.

scholarly journals Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Holger Steiner ◽  
Sebastian Sporrer ◽  
Andreas Kolb ◽  
Norbert Jung
Keyword(s):  
Face Recognition ◽  
Imaging System ◽  
Short Wave ◽  
Classification Performance ◽  
High Spectral Resolution ◽  
Face Verification ◽  
Camera System ◽  
Spectral Signatures ◽  
Face Images ◽  
Small Band

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

Mineral mapping at the Ikh Shankhai porphyry Cu deposits, Mongolia using WorldView-3 data

2020 ◽  
Author(s):  
Son Youngsun ◽  
Kim Kwang-Eun
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Matched Filter ◽  
Extreme Environments ◽  
Ground Truth ◽  
Spectral Signature ◽  
Ground Truth Data ◽  
Mineral Mapping ◽  
Porphyry Cu ◽  
Porphyry Cu Deposit

&lt;p&gt;Southeastern Mongolia has limited access due to its extreme environments (long and harsh winter) and lack of infrastructure (e.g., road). Satellite remote sensing technique is one of the most effective methods to get geological information in areas where field survey is difficult. WorldView-3 (WV3), launched in August 2014, is high-spatial resolution commercial multispectral sensor developed by DigitalGlobe. WV3 measures reflected radiation in eight visible near infrared (VNIR) bands between 0.42 and 1.04 &amp;#13211; and in eight short-wave infrared (SWIR) bands between 1.20 and 2.33, which have 1.24- and 7.5-m spatial resolution, respectively. In this study, WV3 VNIR and SWIR data were used to identify and map the various minerals in the Ikh Shankhai porphyry Cu deposit district, Mongolia.&lt;/p&gt;&lt;p&gt;The Ikh-Shankhai porphyry Cu deposit is located within Gurvansayhan island arc terrane in southeastern (SE) Gobi mineral belt, Mongolia. The Ikh-Shankhai district include the porphyry system containing Cu-Au with primary chalcopyrite, which is classified into disseminated type and stockwork quartz type. This district consists of Late Devonian-Early Carboniferous andesite, tuff and siltstone intruded by Carboniferous-Permian granite, granodiorite and granodiorite porphyry.&lt;/p&gt;&lt;p&gt;The WV 3 data were analyzed using mixture-tuned-matched filter (MTMF) which locates a known spectral signature in the presence of a mixed or unknown background. MTMF does not require knowledge of all of the spectral endmembers and is suited for used where materials with distinct spectral signatures occur within a single pixel. From the WV3 analysis result using mixture-tuned-matched filter (MTMF), we identified the location and abundance of alteration minerals. Advanced argillic minerals (alunite, kaolinite (or dickite), and pyrophyllite) were dominant in the lithocaps of the Budgat and Gashuun Khudag prospects; whereas, phyllic (illite) and propylitic (calcite and epidote) minerals were dominant in the areas surrounding the lithocaps. In addition, the distribution of ferric minerals (hematite and goethite) was mapped because of the oxidation of pyrite. Field work at the Ikh-Shankhai porphyry Cu district to evaluate the accuracy of the mineral mapping results was carried out in August, 2018. Reflectance spectra acquisition using a portable ASD TerraSpec Halo mineral identifier (the attached GPS covered a spectral range of 0.35 &amp;#8211; 2.5 &amp;#181;m) was conducted in the altered outcrops of the Ikh-Shankhai porphyry Cu district. Mineral mapping results compared well with the field spectral measurements collected for the ground truth and demonstrated WV3 capability for identifying and mapping minerals associated with hydrothermal alteration. Evaluation of the WV3 mineral mapping results using ground truth data indicates, however, a difficulty in mapping spectrally similar minerals (e.g., kaolinite and dickite) due to spectral resolution limitation.&lt;/p&gt;

scholarly journals Identification of Phyllosilicates in the Antarctic Environment Using ASTER Satellite Data: Case Study from the Mesa Range, Campbell and Priestley Glaciers, Northern Victoria Land

2020 ◽  
Vol 13 (1) ◽  
pp. 38
Author(s):  
Amin Beiranvand Pour ◽  
Milad Sekandari ◽  
Omeid Rahmani ◽  
Laura Crispini ◽  
Andreas Läufer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Thermal Emission ◽  
Ground Truth ◽  
Xrd Analysis ◽  
Short Wave ◽  
Metamorphic Complex ◽  
Victoria Land ◽  
The Antarctic ◽  
Antarctic Environment

In Antarctica, spectral mapping of altered minerals is very challenging due to the remoteness and inaccessibility of poorly exposed outcrops. This investigation evaluates the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing imagery for mapping and discrimination of phyllosilicate mineral groups in the Antarctic environment of northern Victoria Land. The Mixture-Tuned Matched-Filtering (MTMF) and Constrained Energy Minimization (CEM) algorithms were used to detect the sub-pixel abundance of Al-rich, Fe3+-rich, Fe2+-rich and Mg-rich phyllosilicates using the visible and near-infrared (VNIR), short-wave infrared (SWIR) and thermal-infrared (TIR) bands of ASTER. Results indicate that Al-rich phyllosilicates are strongly detected in the exposed outcrops of the Granite Harbour granitoids, Wilson Metamorphic Complex and the Beacon Supergroup. The presence of the smectite mineral group derived from the Jurassic basaltic rocks (Ferrar Dolerite and Kirkpatrick Basalts) by weathering and decomposition processes implicates Fe3+-rich and Fe2+-rich phyllosilicates. Biotite (Fe2+-rich phyllosilicate) is detected associated with the Granite Harbour granitoids, Wilson Metamorphic Complex and Melbourne Volcanics. Mg-rich phyllosilicates are mostly mapped in the scree, glacial drift, moraine and crevasse fields derived from weathering and decomposition of the Kirkpatrick Basalt and Ferrar Dolerite. Chlorite (Mg-rich phyllosilicate) was generally mapped in the exposures of Granite Harbour granodiorite and granite and partially identified in the Ferrar Dolerite, the Kirkpatrick Basalt, the Priestley Formation and Priestley Schist and the scree, glacial drift and moraine. Statistical results indicate that Al-rich phyllosilicates class pixels are strongly discriminated, while the pixels attributed to Fe3+-rich class, Fe2+-rich and Mg-rich phyllosilicates classes contain some spectral mixing due to their subtle spectral differences in the VNIR+SWIR bands of ASTER. Results derived from TIR bands of ASTER show that a high level of confusion is associated with mafic phyllosilicates pixels (Fe3+-rich, Fe2+-rich and Mg-rich classes), whereas felsic phyllosilicates (Al-rich class) pixels are well mapped. Ground truth with detailed geological data, petrographic study and X-ray diffraction (XRD) analysis verified the remote sensing results. Consequently, ASTER image-map of phyllosilicate minerals is generated for the Mesa Range, Campbell and Priestley Glaciers, northern Victoria Land of Antarctica.

scholarly journals Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura K. Young ◽  
Hannah E. Smithson
Keyword(s):  
Adaptive Optics ◽  
Retinal Image ◽  
Imaging System ◽  
Ground Truth ◽  
Learning Approaches ◽  
Image Capture ◽  
Ground Truth Data ◽  
Cone Mosaic ◽  
Synthetic Images

AbstractHigh resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.

scholarly journals Spatio-Temporal Variation of Vegetation in Godavari Eastern Delta

2019 ◽  
pp. 1-7
Author(s):  
G. Kishore Kumar ◽  
M. Raghu Babu ◽  
A. Mani ◽  
M. Matin Luther ◽  
V. Srinivasa Rao
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
Satellite Image ◽  
Ground Truth ◽  
Likelihood Method ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Wide Range

Spatial variability in land use changes creates a need for a wide range of applications, including landslide, erosion, land planning, global warming etc. This study presents the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) in Godavari eastern delta. Four spectral indices were investigated in this study. These indices were NIR (red and near infrared) based NDVI, green and NIR based GVI (Green Vegetation Index), red and NIR based soil adjusted vegetation index (SAVI), and red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for 2011-12 kharif, rabi and 2016-17 kharif, rabi of Godavari eastern delta. Different threshold values of NDVI are used for generating the false colour composite of the classified objects. For this purpose, supervised classification is applied to Landsat images acquired in 2011-12 and 2016-17. Image classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from satellite images of 2011-12 and 2016-17. There was 11% and 30% increase in vegetation during kharif and rabi seasons from 2011-12 to 2016-17. The vegetation analysis can be used to provide humanitarian aid, damage assessment in case of unfortunate natural disasters and furthermore to device new protection strategies.

scholarly journals Introducing WIW for Detecting the Presence of Water in Wetlands with Landsat and Sentinel Satellites

2019 ◽  
Vol 11 (19) ◽  
pp. 2210 ◽  
Author(s):  
Lefebvre ◽  
Davranche ◽  
Willm ◽  
Campagna ◽  
Redmond ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Ground Truth ◽  
Short Wave ◽  
Water Dynamics ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Model Combining ◽  
Wide Range ◽  
Sentinel 2

Many wetlands are characterized by a vegetation cover of variable height and density over time. Tracking spatio-temporal changes in inundation patterns of these wetlands remains a challenge for remote sensing. Water In Wetlands (WIW) was predicted using a dichotomous partitioning of reflectance values encoded based on ground-truth (n = 4038) and optical-space derived (n = 7016) data covering all land cover types (n = 17) found in the Rhône delta, southern France. The models were developed with spectral data from Sentinel 2, Landsat 7, and Landsat 8 sensors, hence providing a monitoring tool that covers a 35-year period (same sensor for Landsat 5 and 7). A single model combining the near infrared (NIR ≤ 0.1558 to 0.1804, depending on sensors) and short-wave infrared (SWIR2 ≤ 0.0871 to 0.1131) wavelengths was identified by three independent analyses, each one using a different satellite. Overall accuracy of water maps ranged from 89% to 94% for the training samples and from 90% to 94% for the validation samples, encompassing standard water indices that systematically underestimate flooding duration under vegetation cover. Sentinel 2 provided the highest performance with a kappa coefficient of 0.82 for both samples. Such tool will be most useful for monitoring the water dynamics of seasonal wetlands, which are particularly sensitive to climate change while providing multiple services to humankind. Considering the high temporal resolution of Sentinel 2 (every 5 days), cumulative water maps built with the WIW logical rule could further be used for mapping a wide range of wetlands which are either periodically or permanently flooded.

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