Relevant bibliographies by topics / Ground Truth Daten

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Ground Truth Daten'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Ground Truth Daten"

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Han, Xiongzhe, J. Alex Thomasson, G. Cody Bagnall, et al. "Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images." Sensors 18, no. 12 (2018): 4092. http://dx.doi.org/10.3390/s18124092.

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Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth
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Myers, Emily, John Kerekes, Craig Daughtry, and Andrew Russ. "Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting." Remote Sensing 11, no. 21 (2019): 2558. http://dx.doi.org/10.3390/rs11212558.

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Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing
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Pan, Feifei, Xiaohuan Xi, and Cheng Wang. "A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery." Remote Sensing 12, no. 10 (2020): 1611. http://dx.doi.org/10.3390/rs12101611.

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A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blu
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Levitan, Nathaniel, and Barry Gross. "Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms." Remote Sensing 10, no. 12 (2018): 1968. http://dx.doi.org/10.3390/rs10121968.

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Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulat
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Ashmitha Nihar, M., J. Mohammed Ahamed, S. Pazhanivelan, R. Kumaraperumal, and K. Ganesha Raj. "ESTIMATION OF COTTON AND MAIZE CROP AREA IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI-DATE SENTINEL-1A SAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 67–71. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-67-2019.

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<p><strong>Abstract.</strong> Crop classification is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data has an advantage in crop classification because of its all-weather imaging capabilities. The objective of this study was to investigate the capability of SAR data for estimation of cotton and maize area in Perambalur district of Tamil Nadu. The multi-temporal Sentinel-1 SAR data was acquired from 2nd September, 2017 to 24th January, 2018. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data was
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Kumar, P., S. K. Rajpoot, V. Jain, S. Saxena, and S. S. Ray. "MONITORING OF RICE CROP IN PUNJAB AND HARYANA WITH RESPECT TO RESIDUE BURNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 31–36. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-31-2019.

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<p><strong>Abstract.</strong> This study is based on the fortnightly monitoring of residue burning in fields of <i>kharif</i> rice crops after harvesting in Punjab and Haryana. The <i>kharif</i> rice area (2018–19) was derived using multi-date Sentinel-1A(Microwave SAR) data under FASAL project. The expected harvesting dates of <i>kharif</i> rice (crop duration 140 days) are derived on the basis of transplanting dates (derived from SAR data), NDVI profiles and Ground truth data. Daily active crop fire events in Punjab and Hary
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Garcia-Pedrero, Angel, Consuelo Gonzalo-Martin, Mario Lillo-Saavedra, and Dionisio Rodríguez-Esparragón. "The Outlining of Agricultural Plots Based on Spatiotemporal Consensus Segmentation." Remote Sensing 10, no. 12 (2018): 1991. http://dx.doi.org/10.3390/rs10121991.

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The outlining of agricultural land is an important task for obtaining primary information used to create agricultural policies, estimate subsidies and agricultural insurance, and update agricultural geographical databases, among others. Most of the automatic and semi-automatic methods used for outlining agricultural plots using remotely sensed imagery are based on image segmentation. However, these approaches are usually sensitive to intra-plot variability and depend on the selection of the correct parameters, resulting in a poor performance due to the variability in the shape, size, and textu
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Guan, X., W. Qi, J. He, Q. Wen, T. Chen, and Z. Wang. "PURIFICATION OF TRAINING SAMPLES BASED ON SPECTRAL FEATURE AND SUPERPIXEL SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 425–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-425-2018.

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Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manuall
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Pádua, Luís, Pedro Marques, Jonáš Hruška, et al. "Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery." Remote Sensing 10, no. 12 (2018): 1907. http://dx.doi.org/10.3390/rs10121907.

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This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to comp
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Wendleder, A., A. Heilig, A. Schmitt, and C. Mayer. "MONITORING OF WET SNOW AND ACCUMULATIONS AT HIGH ALPINE GLACIERS USING RADAR TECHNOLOGIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1063–68. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1063-2015.

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Conventional studies to assess the annual mass balance for glaciers rely on single point observations in combination with model and interpolation approaches. Just recently, airborne and spaceborne data is used to support such mass balance determinations. Here, we present an approach to map temporal changes of the snow cover in glaciated regions of Tyrol, Austria, using SAR-based satellite data. Two dual-polarized SAR images are acquired on 22 and 24 September 2014. As X and C-band reveal different backscattering properties of snow, both TerraSAR-X and RADARSAT-2 images are analysed and compare
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Dissertations / Theses on the topic "Ground Truth Daten"

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Möhrmann, Julia Mandy. "Effiziente Erstellung aufgabenspezifischer Bilderkennungssysteme." Doctoral thesis, 2014. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2014072912641.

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Die Entwicklung von Bilderkennungssystemen ist ein hochkomplexer Prozess, der derzeit fast ausschließlich von Experten mit entsprechenden Fachkenntnissen bewältigt werden kann. Bilderkennungssysteme haben durch die allgegenwärtige Verfügbarkeit an Bilddaten und ihren kostenneutralen Einsatz das Potential, eine wichtige Rolle in der digitalisierten Gesellschaft zu spielen. Jedoch ist ihr Einsatz effektiv durch den nicht vorhandenen Zugang zu entsprechenden Entwicklungswerkzeugen limitiert. Diese Arbeit verfolgt das Ziel, ein Software-Framework bereitzustellen, das von Anwendern ohne Fachkenntni
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Conference papers on the topic "Ground Truth Daten"

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Kydd, Kris, Dervla Brennan, Neil Kirkpatrick, and Matthew Wright. "Robotics, Digital Twins and AI: Connecting the Dot Matrix." In SPE Offshore Europe Conference & Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205409-ms.

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Abstract Robotics is often referred to as an enabler towards safer and more cost-effective operations within the energy industry. However, for robots to achieve their full potential they too require their own enablers. This paper intends to present a collection of lessons learnt in robotic development to date that has resulted in the necessity to develop an encompassing digital architecture. This architecture, with an artificial intelligence component, has been designed to optimise both robot and digital twin capability, where the complete system is always working with the latest information a
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Paradis, Olivia P., Nathan T. Jessurun, Mark Tehranipoor, and Navid Asadizanjani. "Color Normalization for Robust Automatic Bill of Materials Generation and Visual Inspection of PCBs." In ISTFA 2020. ASM International, 2020. http://dx.doi.org/10.31399/asm.cp.istfa2020p0172.

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Abstract A Bill of Materials (BoM) is the list of all components present on a Printed Circuit Board (PCB). BoMs are useful for multiple forms of failure analysis and hardware assurance. In this paper, we build upon previous work and present an updated framework to automatically extract a BoM from optical images of PCBs in order to keep up to date with technological advancements. This is accomplished by revising the framework to emphasize the role of machine learning and by incorporating domain knowledge of PCB design and hardware Trojans. For accurate machine learning methods, it is critical t
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Journal articles
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