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Relevant bibliographies by topics / UAV-borne remote sensing

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Academic literature on the topic 'UAV-borne remote sensing'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "UAV-borne remote sensing"

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Bareth, G., and C. Hütt. "UPSCALING AND VALIDATION OF RTK-DIRECT GEOREFERENCED UAV-BASED RGB IMAGE DATA WITH PLANET IMAGERY USING POLYGON GRIDS FOR PASTURE MONITORING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 533–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-533-2021.

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Abstract. The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techniques or in the focus of crop and grassland monitoring and provide very high spatial resolutions. In contrast, satellite data are only available in high to moderate spatial resolutions. In this contribution, we introduce direct georeferenced

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Wei, Lifei, Ming Yu, Yajing Liang, et al. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (2019): 2011. http://dx.doi.org/10.3390/rs11172011.

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The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a pre

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Liu, Hong, Tao Yu, Bingliang Hu, et al. "UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring." Remote Sensing 13, no. 20 (2021): 4069. http://dx.doi.org/10.3390/rs13204069.

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Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing sy

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Dadrass Javan, Farzaneh, Farhad Samadzadegan, Ahmad Toosi, and Mark van der Meijde. "Unmanned Aerial Geophysical Remote Sensing: A Systematic Review." Remote Sensing 17, no. 1 (2024): 110. https://doi.org/10.3390/rs17010110.

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Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar

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Wei, Lifei, Can Huang, Yanfei Zhong, Zhou Wang, Xin Hu, and Liqun Lin. "Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 12 (2019): 1455. http://dx.doi.org/10.3390/rs11121455.

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Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spe

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Harder, Phillip, Warren D. Helgason, and John W. Pomeroy. "Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar." Cryosphere 18, no. 7 (2024): 3277–95. http://dx.doi.org/10.5194/tc-18-3277-2024.

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Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spa

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Wei, Lifei, Zhou Wang, Can Huang, et al. "Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery." IEEE Access 8 (2020): 168137–53. http://dx.doi.org/10.1109/access.2020.3023690.

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Zhong, Yanfei, Xinyu Wang, Yao Xu, et al. "Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications." IEEE Geoscience and Remote Sensing Magazine 6, no. 4 (2018): 46–62. http://dx.doi.org/10.1109/mgrs.2018.2867592.

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Liu, C., X. Zhou, Y. Zhou, and A. Akbar. "MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1469–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1469-2020.

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Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This

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Natesan, S., G. Benari, C. Armenakis, and R. Lee. "LAND COVER CLASSIFICATION USING A UAV-BORNE SPECTROMETER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 24, 2017): 269–73. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-269-2017.

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Small fixed wing and rotor-copter unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification and precision agriculture applications. Various sensors operating in the non-visible spectrum such as multispectral, hyperspectral and thermal sensors can be used as payloads. This work presents a preliminary study on the use of unmanned aerial vehicle equipped with a compact spectrometer for land cover type characterization. When calibrated, the measured spectra by the UAV spectrometer can be processed and compared reference data to generate georefer

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Book chapters on the topic "UAV-borne remote sensing"

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Vasterling, Margarete, and Uwe Meyer. "Challenges and Opportunities for UAV-Borne Thermal Imaging." In Thermal Infrared Remote Sensing. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6639-6_4.

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Caroti, Gabriella, Andrea Piemonte, and Yari Pieracci. "Low-Altitude UAV-Borne Remote Sensing in Dunes Environment: Shoreline Monitoring and Coastal Resilience." In Computational Science and Its Applications – ICCSA 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95174-4_23.

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Conference papers on the topic "UAV-borne remote sensing"

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Zhu, Jinbiao, Mingqian Liu, Bei Lin, et al. "UAV-Borne Bistatic Interferometric SAR Time-Phase Synchronization Technology Based on Bi-Directional Synchronization Chain." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641778.

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Wang Feng, Wu Yundong, and Zhang Qiang. "UAV borne real-time road mapping system." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137476.

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Zhong, Yanfei, Xinyu Wang, Yao Xu, et al. "MINI-UAV borne hyperspectral remote sensing: A review." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128354.

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Samberg, Andre, Taras Kazantsev, Fevzi Ametov, et al. "COTS UAV-borne multispectral system for vegetation monitoring." In Remote Sensing for Agriculture, Ecosystems, and Hydrology, edited by Christopher M. Neale and Antonino Maltese. SPIE, 2018. http://dx.doi.org/10.1117/12.2501859.

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Herschel, Reinhold, Patrick Wallrath, Michael Hofstätter, et al. "UAV-borne remote sensing for AI-assisted support of search and rescue missions." In Electro-Optical Remote Sensing XVI, edited by Gary W. Kamerman and Ove Steinvall. SPIE, 2022. http://dx.doi.org/10.1117/12.2636032.

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Teisberg, Thomas O., Dustin M. Schroeder, Anna L. Broome, Franklin Lurie, and Dennis Woo. "Development of a Uav-Borne Pulsed ICE-Penetrating Radar System." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883583.

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Qu, Jiameng, Manlai Ding, Bingnan Wang, et al. "An Ultra-Small Real-Time Imaging System for UAV Borne SAR." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10282709.

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Zhang, Xiangkun, Zelong Shao, Jiawei Ren, Yingsong Li, and Jingshan Jiang. "Development and Preliminary Results of Small-Size Uav-Borne Fmcw Sar." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8519235.

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Cherid, Ali, Noureddine Aribi, Mohammed Said Belaid, and Yahia Lebbah. "Efficient UAV-Borne Hyperspectral Image Classification Using Spatial Guided Filtering." In 2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2024. http://dx.doi.org/10.1109/m2garss57310.2024.10537503.

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Frey, Othmar, Charles L. Werner, Andrea Manconi, and Roberto Coscione. "Measurement of surface displacements with a UAV-borne/car-borne L-band DInSAR system: system performance and use cases." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553573.

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