Relevant bibliographies by topics / Multispectral aerial video imagery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multispectral aerial video imagery'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Multispectral aerial video imagery"

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Morozov, A. N., A. L. Nazolin, and I. L. Fufurin. "Optical and Spectral Methods for Detection and Recognition of Unmanned Aerial Vehicles." Radio Engineering, no. 2 (May 17, 2020): 39–50. http://dx.doi.org/10.36027/rdeng.0220.0000167.

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The paper considers a problem of detection and identification of unmanned aerial vehicles (UAVs) against the animate and inanimate objects and identification of their load by optical and spectral optical methods. The state-of-the-art analysis has shown that, when using the radar methods to detect small UAVs, there is a dead zone for distances of 250-700 m, and in this case it is important to use optical methods for detecting UAVs.The application possibilities and improvements of the optical scheme for detecting UAVs at long distances of about 1-2 km are considered. Location is performed by int
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Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. "ACCURACY ASSESSMENT OF DIRECT GEOREFERENCING FOR PHOTOGRAMMETRIC APPLICATIONS ON SMALL UNMANNED AERIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 77–83. http://dx.doi.org/10.5194/isprs-archives-xl-3-w4-77-2016.

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Efficient mapping from unmanned aerial platforms cannot rely on aerial triangulation using known ground control points. The cost and time of setting ground control, added to the need for increased overlap between flight lines, severely limits the ability of small VTOL platforms, in particular, to handle mapping-grade missions of all but the very smallest survey areas. Applanix has brought its experience in manned photogrammetry applications to this challenge, setting out the requirements for increasing the efficiency of mapping operations from small UAVs, using survey-grade GNSS-Inertial techn
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Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. "ACCURACY ASSESSMENT OF DIRECT GEOREFERENCING FOR PHOTOGRAMMETRIC APPLICATIONS ON SMALL UNMANNED AERIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 77–83. http://dx.doi.org/10.5194/isprsarchives-xl-3-w4-77-2016.

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Efficient mapping from unmanned aerial platforms cannot rely on aerial triangulation using known ground control points. The cost and time of setting ground control, added to the need for increased overlap between flight lines, severely limits the ability of small VTOL platforms, in particular, to handle mapping-grade missions of all but the very smallest survey areas. Applanix has brought its experience in manned photogrammetry applications to this challenge, setting out the requirements for increasing the efficiency of mapping operations from small UAVs, using survey-grade GNSS-Inertial techn
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Jayroe, Clinton W., William H. Baker, and Amy B. Greenwalt. "Using Multispectral Aerial Imagery to Evaluate Crop Productivity." Crop Management 4, no. 1 (2005): 1–7. http://dx.doi.org/10.1094/cm-2005-0205-01-rs.

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Bruce, Robert W., Istvan Rajcan, and John Sulik. "Classification of Soybean Pubescence from Multispectral Aerial Imagery." Plant Phenomics 2021 (August 4, 2021): 1–11. http://dx.doi.org/10.34133/2021/9806201.

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The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescen
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Yang, Bo, Timothy L. Hawthorne, Hannah Torres, and Michael Feinman. "Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data." Drones 3, no. 3 (2019): 60. http://dx.doi.org/10.3390/drones3030060.

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have qu
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Kramber‡, W. J., A. J. Richardson§, P. R. Nixon§, and K. Lulla†. "Principal component analysis of aerial video imagery†." International Journal of Remote Sensing 9, no. 9 (1988): 1415–22. http://dx.doi.org/10.1080/01431168808954949.

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Zhang, Yanchao, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, and Wenbo Zhang. "Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification." Remote Sensing 13, no. 8 (2021): 1411. http://dx.doi.org/10.3390/rs13081411.

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Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine lear
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Yang, Chenghai, Charles P. C. Suh, and John K. Westbrook. "Early identification of cotton fields using mosaicked aerial multispectral imagery." Journal of Applied Remote Sensing 11, no. 1 (2017): 016008. http://dx.doi.org/10.1117/1.jrs.11.016008.

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Soni, Ayush, Alexander Loui, Scott Brown, and Carl Salvaggio. "High-quality multispectral image generation using Conditional GANs." Electronic Imaging 2020, no. 8 (2020): 86–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.8.imawm-086.

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In this paper, we demonstrate the use of a Conditional Generative Adversarial Networks (cGAN) framework for producing high-fidelity, multispectral aerial imagery using low-fidelity imagery of the same kind as input. The motivation behind is that it is easier, faster, and often less costly to produce low-fidelity images than high-fidelity images using the various available techniques, such as physics-driven synthetic image generation models. Once the cGAN network is trained and tuned in a supervised manner on a data set of paired low- and high-quality aerial images, it can then be used to enhan
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Dissertations / Theses on the topic "Multispectral aerial video imagery"

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Hodgson, Lucien Guy, and n/a. "Cotton crop condition assessment using arial video imagery." University of Canberra. Applied Science, 1991. http://erl.canberra.edu.au./public/adt-AUC20060725.144909.

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Cotton crop condition was assessed from an analysis of multispectral aerial video imagery. Visible-near infrared imagery of two cotton fields was collected towards the end of the 1990 crop. The digital analysis was based on image classification, and the accuracies were assessed using the Kappa coefficient of agreement. The earliest of three images proved to be best for distinguishing plant variety. Vegetation index images were better for estimating potential yield than the original multispectral image; so too were multi-channel images that were transformed using vegetation indices or principal
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Gurram, Prudhvi K. "Automated 3D object modeling from aerial video imagery /." Online version of thesis, 2009. http://hdl.handle.net/1850/11207.

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Salmon, Summer Anne. "A New Technique for Measuring Runup Variation Using Sub-Aerial Video Imagery." The University of Waikato, 2008. http://hdl.handle.net/10289/2511.

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Video monitoring of beaches is becoming the preferred method for observing changes to nearshore morphology. Consequently this work investigates a new technique for predicting the probability of inundation that is based on measuring runup variation using video. Runup is defined as the water-level elevation maxima on the foreshore relative to the still water level and the waterline is defined as the position where the MWL intersects the beach face. Tairua, and Pauanui Beaches, on the north east coast of the North Island of New Zealand, were used as the field site in this study and represent two
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Wolkesson, Henrik. "Realtime Mosaicing of Video Stream from µUAV." Thesis, Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76357.

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This is a master thesis of the Master of Science degree program in Applied Physics and Electrical Engineering (Y) at Linköping University. The goal of the projectis to develop an application for creating a map in real time from a video camera on a miniature unmanned aerial vehicle. This thesis project and report is a first exploratory study for this application. It implements a prototype method and evaluates it on sample sequences from an on-board video camera. The method first looks for good points to follow in the image and then tracks them in a sequence.The image is then pasted, or merged,
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Potter, Thomas Noel 1959. "The use of multispectral aerial video to determine land cover for hydrological simulations in small urban watersheds." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/291381.

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Airborne multispectral video was evaluated as a tool for obtaining urban land cover information for hydrological simulations. Land cover data was obtained for a small urban watershed in Tucson, Arizona using four methods: multispectral aerial video (2 meter and 4 meter pixel resolution), National High Altitude Photography (NHAP), multispectral satellite imagery from Systeme Pour l'Observation de la Terre (SPOT), and by conventional survey. A semi-automated land cover classification produced four classes: vegetation, buildings, pavement, and bare soil. The land cover data from each classificati
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Maier, Kathrin. "Direct multispectral photogrammetry for UAV-based snow depth measurements." Thesis, KTH, Geoinformatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254566.

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Due to the changing climate and inherent atypically occurring meteorological events in the Arctic regions, more accurate snow quality predictions are needed in order to support the Sámi reindeer herding communities in northern Sweden that struggle to adapt to the rapidly changing Arctic climate. Spatial snow depth distribution is a crucial parameter not only to assess snow quality but also for multiple environmental research and social land use purposes. This contrasts with the current availability of affordable and efficient snow monitoring methods to estimate such an extremely variable para
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Apostolopoulos, Andreas K. Tisdale Riley O. "Dissemination and storage of tactical unmanned aerial vehicle digital video imagery at the Army Brigade Level /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA374041.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1999.<br>"September 1999". Thesis advisor(s): Orin E. Marvel, William Haga, Brad Naegle. Includes bibliographical references (p. 159-162). Also avaliable online.
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Apostolopoulos, Andreas K., and Riley O. Tisdale. "Dissemination and storage of tactical unmanned aerial vehicle digital video imagery at the Army Brigade Level." Thesis, Monterey, California. Naval Postgraduate School, 1999. http://hdl.handle.net/10945/26490.

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Approved for public release; distribution is unlimited<br>The Department of Defense Joint Technical Architecture has mandated a migration from analog to digital technology in the Command, Control, Communication, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) community. The Tactical Unmanned Aerial Vehicle (TUAV) and Tactical Control System (TCS) are two brigade imagery intelligence systems that the Army will field within the next three years to achieve information superiority on the modern digital battlefield. These two systems provide the brigade commander with an imagery c
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Heiner, Benjamin Kurt. "Construction of Large Geo-Referenced Mosaics from MAV Video and Telemetry Data." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1804.

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Miniature Aerial Vehicles (MAVs) are quickly gaining acceptance as a platform for performing remote sensing or surveillance of remote areas. However, because MAVs are typically flown close to the ground (1000 feet or less in altitude), their field of view for any one image is relatively small. In addition, the context of the video (where and at what orientation are the objects being observed, the relationship between images) is unclear from any one image. To overcome these problems, we propose a geo-referenced mosaicing method that creates a mosaic from the captured images and geo-references t
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Andersen, Evan D. "A Surveillance System to Create and Distribute Geo-Referenced Mosaics Using SUAV Video." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1679.

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Small Unmanned Aerial Vehicles (SUAVs) are an attractive choice for many surveillance tasks. However, video from an SUAV can be difficult to use in its raw form. In addition, the limitations inherent in the SUAV platform inhibit the distribution of video to remote users. To solve the problems with using SUAV video, we propose a system to automatically create geo-referenced mosiacs of video frames. We also present three novel techniques we have developed to improve ortho-rectification and geo-location accuracy of the mosaics. The most successful of these techniques is able to reduce geo-locatio
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Books on the topic "Multispectral aerial video imagery"

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Apostolopoulos, Andreas K. Dissemination and storage of tactical unmanned aerial vehicle digital video imagery at the Army Brigade Level. Naval Postgraduate School, 1999.

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King, Douglas John. Development of a multispectral aerial video system and its application in forest and land cover type analysis. 1988.

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Dissemination and Storage of Tactical Unmanned Aerial Vehicle Digital Video Imagery at the Army Brigade Level. Storming Media, 1999.

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Book chapters on the topic "Multispectral aerial video imagery"

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Brauchle, Jörg, Steven Bayer, and Ralf Berger. "Automatic Ship Detection on Multispectral and Thermal Infrared Aerial Images Using MACS-Mar Remote Sensing Platform." In Image and Video Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92753-4_30.

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Messina, Gaetano, Vincenzo Fiozzo, Salvatore Praticò, et al. "Monitoring Onion Crops Using Multispectral Imagery from Unmanned Aerial Vehicle (UAV)." In New Metropolitan Perspectives. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48279-4_154.

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Chen, Liang-Chien, Tee-Ann Teo, Chi-Heng Hsieh, and Jiann-Yeou Rau. "Reconstruction of Building Models with Curvilinear Boundaries from Laser Scanner and Aerial Imagery." In Advances in Image and Video Technology. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_3.

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Vasile, Alexandru N., Luke J. Skelly, Karl Ni, Richard Heinrichs, and Octavia Camps. "Efficient City-Sized 3D Reconstruction from Ultra-High Resolution Aerial and Ground Video Imagery." In Advances in Visual Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_32.

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Kuhnert, Lars, Markus Ax, Matthias Langer, Duong Nguyen Van, and Klaus-Dieter Kuhnert. "Absolute High-Precision Localisation of an Unmanned Ground Vehicle by Using Real-Time Aerial Video Imagery for Geo-referenced Orthophoto Registration." In Autonome Mobile Systeme 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10284-4_19.

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"Benthic Habitats and the Effects of Fishing." In Benthic Habitats and the Effects of Fishing, edited by T. D. Clayton, J. C. Brock, and C. W. Wright. American Fisheries Society, 2005. http://dx.doi.org/10.47886/9781888569605.ch21.

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For ecologists and managers of seagrass systems, the spatial context provided by remote sensing has proven to be an important complement to in situ assessments and measurements. The spatial extent of seagrass beds has been mapped most commonly with conventional aerial photography. Additional remote mapping and monitoring tools applied to seagrass studies include optical satellite sensors, airborne multispectral scanners, underwater video cameras, and towed sonar systems. An additional tool that shows much promise is airborne, waveform-resolving lidar (light detection and ranging). Now used routinely for high-resolution bathymetric and topographic surveys, lidar systems operate by emitting a laser pulse, then measuring its two-way travel time from the plane to reflecting surface(s) below, then back to the detector co-located with the laser transmitter. Using a novel, waveformresolving lidar system developed at NASA — the Experimental Advanced Airborne Research Lidar (EAARL) — we are investigating the possibility of using the additional information contained in the returned laser pulse (waveform) for the purposes of benthic habitat mapping. Preliminary analyses indicate that seagrass beds can potentially be delineated on the basis of apparent bathymetry, returned waveform shape and amplitude, and (horizontal) spatial texture. A complete set of georectified digital camera imagery is also collected during each EAARL overflight and can aid in mapping efforts. Illustrative examples are shown from seagrass beds in the turbid waters of Tampa Bay and the relatively clear waters of the Florida Keys.
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Rango, Albert, and Jerry Ritchie. "Applications of Remotely Sensed Data from the Jornada Basin." In Structure and Function of a Chihuahuan Desert Ecosystem. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195117769.003.0019.

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Like other rangelands, little application of remote sensing data for measurement and monitoring has taken place within the Jornada Basin. Although remote sensing data in the form of aerial photographs were acquired as far back as 1935 over portions of the Jornada Basin, little reliance was placed on these data. With the launch of Earth resources satellites in 1972, a variety of sensors have been available to collect remote sensing data. These sensors are typically satellite-based but can be used from other platforms including ground-based towers and hand-held apparatus, low-altitude aircraft, and high-altitude aircraft with various resolutions (now as good as 0.61 m) and spectral capabilities. A multispectral, multispatial, and multitemporal remote sensing approach would be ideal for extrapolating ground-based point and plot knowledge to large areas or landscape units viewed from satellite-based platforms. This chapter details development and applications of long-term remotely sensed data sets that are used in concert with other long-term data to provide more comprehensive knowledge for management of rangeland across this basin and as a template for their use for rangeland management in other regions. In concert with the ongoing Jornada Basin research program of ground measurements, in 1995 we began to collect remotely sensed data from ground, airborne, and satellite platforms to provide spatial and temporal data on the physical and biological state of basin rangeland. Data on distribution and reflectance of vegetation were measured on the ground along preestablished transects with detailed vegetation surveys (cover, composition, and height); with hand-held and yoke-mounted spectral and thermal radiometers; from aircraft flown at different elevations with spectral and thermal radiometers, infrared thermal radiometers, multispectral video, digital imagers, and laser altimeters; and from space with Landsat Thematic Mapper (TM), IKONOS, QuickBird, Terra/Aqua, and other satellite-based sensors. These different platforms (ground, aircraft, and satellite) allow evaluation of landscape patterns and states at different scales. One general use of these measurements will be to quantify the hydrologic budget and plant response to changes in components in the water and energy balance at different scales and to evaluate techniques of scaling data.
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Mathews, Adam J. "A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards." In Unmanned Aerial Vehicles. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8365-3.ch012.

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This paper explores the use of compact digital cameras to remotely estimate spectral reflectance based on unmanned aerial vehicle imagery. Two digital cameras, one unaltered and one altered, were used to collect four bands of spectral information (blue, green, red, and near-infrared [NIR]). The altered camera had its internal hot mirror removed to allow the sensor to be additionally sensitive to NIR. Through on-ground experimentation with spectral targets and a spectroradiometer, the sensitivity and abilities of the cameras were observed. This information along with on-site collected spectral data were used to aid in converting aerial imagery digital numbers to estimates of scaled surface reflectance using the empirical line method. The resulting images were used to create spectrally-consistent orthophotomosaics of a vineyard study site. Individual bands were subsequently validated with in situ spectroradiometer data. Results show that red and NIR bands exhibited the best fit (R2: 0.78 for red; 0.57 for NIR).
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Mathews, Adam J. "A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards." In Geospatial Intelligence. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch014.

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This paper explores the use of compact digital cameras to remotely estimate spectral reflectance based on unmanned aerial vehicle imagery. Two digital cameras, one unaltered and one altered, were used to collect four bands of spectral information (blue, green, red, and near-infrared [NIR]). The altered camera had its internal hot mirror removed to allow the sensor to be additionally sensitive to NIR. Through on-ground experimentation with spectral targets and a spectroradiometer, the sensitivity and abilities of the cameras were observed. This information along with on-site collected spectral data were used to aid in converting aerial imagery digital numbers to estimates of scaled surface reflectance using the empirical line method. The resulting images were used to create spectrally-consistent orthophotomosaics of a vineyard study site. Individual bands were subsequently validated with in situ spectroradiometer data. Results show that red and NIR bands exhibited the best fit (R2: 0.78 for red; 0.57 for NIR).
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Essa, Almabrok, Paheding Sidike, and Vijayan K. Asari. "Efficient Key Frame Selection Approach for Object Detection in Wide Area Surveillance Applications." In Censorship, Surveillance, and Privacy. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7113-1.ch032.

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This paper presents an efficient preprocessing algorithm for object detection in wide area surveillance video analysis. The proposed key-frame selection method utilizes the pixel intensity differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames. For improving effectiveness and efficiency, a batch updating based on a modular representation strategy is also incorporated. Experimental results show that the proposed key frame selection technique has a significant positive performance impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery.
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Conference papers on the topic "Multispectral aerial video imagery"

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Chai, Dengfeng, and Qunsheng Peng. "Spatiotemporal alignment of multi-sensor aerial video sequences." In MIPPR 2005 SAR and Multispectral Image Processing, edited by Liangpei Zhang, Jianqing Zhang, and Mingsheng Liao. SPIE, 2005. http://dx.doi.org/10.1117/12.654910.

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Salvado, Ana Beatriz, Ricardo Mendonca, Andre Lourenco, et al. "Semantic Navigation Mapping from Aerial Multispectral Imagery." In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). IEEE, 2019. http://dx.doi.org/10.1109/isie.2019.8781301.

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Fitzgerald, G. J., D. J. Hunsaker, E. M. Barnes, T. R. Clarke, R. Roth, and P. J. Pinter, Jr. "Estimating Cotton Crop Water Use from Multispectral Aerial Imagery, 2003." In World Water and Environmental Resources Congress 2005. American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40792(173)525.

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Williams, Elmer, Michael A. Pusateri, and David Siviter. "Multicamera-multispectral video library - An algorithm development tool." In 2008 37th IEEE Applied Imagery Pattern Recognition Workshop. IEEE, 2008. http://dx.doi.org/10.1109/aipr.2008.4906477.

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Hassan-Esfahani, Leila, Alfonso Torres-Rua, Andres M. Ticlavilca, Austin Jensen, and Mac McKee. "Topsoil moisture estimation for precision agriculture using unmmaned aerial vehicle multispectral imagery." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947175.

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Rottensteiner, F., J. Trinder, S. Clode, K. Kubik, and B. Lovell. "Building detection by Dempster-Shafer fusion of LIDAR data and multispectral aerial imagery." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334203.

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Vasu, Bhavan, Faiz Ur Rahman, and Andreas Savakis. "Aerial-CAM: Salient Structures and Textures in Network Class Activation Maps of Aerial Imagery." In 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, 2018. http://dx.doi.org/10.1109/ivmspw.2018.8448567.

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Viguier, Raphael, Chung Ching Lin, Hadi AliAkbarpour, et al. "Automatic Video Content Summarization Using Geospatial Mosaics of Aerial Imagery." In 2015 IEEE International Symposium on Multimedia (ISM). IEEE, 2015. http://dx.doi.org/10.1109/ism.2015.124.

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Loveland, Rohan C., and Edward Rosten. "Acquisition and registration of aerial video imagery of urban traffic." In Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2008. http://dx.doi.org/10.1117/12.796785.

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Al-Arab, Manal, Alfonso Torres-Rua, Andres Ticlavilca, Austin Jensen, and Mac McKee. "Use of high-resolution multispectral imagery from an unmanned aerial vehicle in precision agriculture." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723419.

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Reports on the topic "Multispectral aerial video imagery"

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Cooke, B., and A. Saucier. Correction of Line Interleaving Displacement in Frame Captured Aerial Video Imagery. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1995. http://dx.doi.org/10.2737/so-rn-380.

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42003.

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Abstract:
Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI)
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