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Journal articles on the topic 'Aerial videography Remote sensing'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Everitt, James H., James V. Richerson, Mario A. Alaniz, David E. Escobar, Ricardo Villarreal, and Michael R. Davis. "Light Reflectance Characteristics and Remote Sensing of Big Bend Loco (Astragalus mollissimusvar.earlei) and Wooton Loco (Astragalus wootonii)." Weed Science 42, no. 1 (March 1994): 115–22. http://dx.doi.org/10.1017/s0043174500084265.

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The high near-infrared reflectance (0.76 to 0.90 μm) of Big Bend loco and Wooton loco contributed significantly to their orange-red and red image tonal responses, respectively, on color-infrared aerial photographs making them distinguishable from associated vegetation and soil. Big Bend loco could also be distinguished on color-infrared and near-infrared black-and-white video imagery where it had distinct red and whitish tonal responses, respectively. Computer analyses of photographic and videographic images showed that Big Bend loco and Wooton loco populations could be quantified from other landscape features. A global positioning system was integrated with the video imagery that permitted latitude-longitude coordinates to appear on each image. The latitude-longitude data were integrated with a geographical information system to map Big Bend loco populations.

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Everitt, James H., David E. Escobar, Ricardo Villarreal, Mario A. Alaniz, and Michael R. Davis. "Canopy Light Reflectance and Remote Sensing of Shin Oak (Quercus havardii) and Associated Vegetation." Weed Science 41, no. 2 (June 1993): 291–97. http://dx.doi.org/10.1017/s0043174500076190.

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Shin oak is a deciduous shrub that forms dense stands of brush on sandy soils in rangeland areas of the Rolling and High Plains of Texas. Plant canopy reflectance measurements made on shin oak showed that it had both low visible (0.63- to 0.69-μm waveband) and nearinfrared (0.76- to 0.90-μm waveband) reflectance values, a characteristic generally not shared by associated plant species or mixtures of species. The low reflectance values of shin oak caused it to have dark-red, reddish-brown, or brown image tones on color-infrared photographic, videographic, and SPOT satellite images that made it distinguishable from associated vegetation and other land use features. The optimum time to remotely distinguish this noxious shrub is during the mature phenological stage from June to September. Computer-based image analyses of video and satellite images showed that shin oak populations could be quantified. This technique can permit “percent land area” estimates of shin oak on rangelands. The aerial imagery is useful for detecting shin oak on smaller rangeland areas, whereas the satellite imagery is applicable in mapping large areas of shin oak distribution.

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3

Kaimaris, Dimitris, Charalampos Georgiadis, Petros Patias, and Vassilis Tsioukas. "Aerial and Remote Sensing Archaeology." International Journal of Computational Methods in Heritage Science 1, no. 1 (January 2017): 58–76. http://dx.doi.org/10.4018/ijcmhs.2017010104.

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New techniques and methodological procedures, which would allow at a short time and at low cost for the identification of a new archaeological site, were always in the interest of archaeologists. In this paper, aerial and remote sensing archaeology issues will be analyzed, both as measuring tools for the documentation of existing archaeological structures as well as tools of archaeology prospection, which are based on the appearance of the reflection of covered structures in images, i.e. the so-called marks.

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Hickman, Michael V., James H. Everitt, David E. Escobar, and Arthur J. Richardson. "Aerial Photography and Videography for Detecting and Mapping Dicamba Injury Patterns." Weed Technology 5, no. 4 (December 1991): 700–706. http://dx.doi.org/10.1017/s0890037x00033728.

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Field trials were conducted to compare on-site visual evaluations with color-infrared photography (CIR), and near-infrared (NIR) videography (video) and hand-held radiometry (HHR) for detecting and mapping dicamba injury in cotton. CIR, video, and HHR detected 48%, 42% and 36%, respectively, of the injured crop area as defined by visual evaluation (injury ratings >0 on a scale of 0 to 9). The remote techniques were unable to differentiate crop injury that did not involve the entire plant canopy. Reflectance measurements in the visible red (R) (630–690 nm) and NIR (760–900 nm) wavelengths were taken and used in herbicide dosage prediction equations. Predicted herbicide dosages were significantly, positively correlated (P≤0.01) with physical measures of herbicide present. These studies suggest that remote detection and mapping of moderate and severe herbicide injury is possible. Further, NIR videography, with near-real-time capability and low cost may be the system of choice for this type of application.

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Mohamed Ali, Abbas Sayed Ahmed, and Ahmed Abu Al Qasim Al Hassan. "Remote Sensing and Its Uses in Archeology." Journal of Arts and Social Sciences [JASS] 2, no. 1 (January 1, 2011): 5. http://dx.doi.org/10.24200/jass.vol2iss1pp5-25.

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Aerial photography, remote sensing technique has been used as a tool for acquisition of archaeological information for several decades. At the turn of the twentieth century, archaeologists realized that valuable archaeological data could be extracted from aerial photos, thus it has been developed into a systematic discipline known as aerial archaeology. Though aerial photography has a long history of use, Satellite remote sensing is a recent discipline applied in detection, mapping and analysis of archaeological matter, providing that the spatial resolution of the sensor is adequate to detect the features. Both aerial photography and satellite imagery have advantages and limitations with regard to archaeological applications. In the last few years, combination of the two was found to be ideal for archaeological remote sensing applications. Remote sensing has increased in importance to archaeology, as being an important close integrator with Geographic Information Systems. Remote sensing and its kindred tool of GIS have become central elements of modern spatial information and analysis system in archaeology.

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Rajan, Nithya, Naveen Puppala, Stephan Maas, Paxton Payton, and Russell Nuti. "Aerial Remote Sensing of Peanut Ground Cover." Agronomy Journal 106, no. 4 (July 2014): 1358–64. http://dx.doi.org/10.2134/agronj13.0532.

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Yang, Guo Dong, Guang Nan Liu, and Yuan Yuan Zhang. "Implementation of Airborne Remote Sensing Route Design System." Advanced Materials Research 588-589 (November 2012): 1171–77. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1171.

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Route design is an important part of airborne remote sensing. On the basis of DEM, using raster and vector geographic information, designing aerial route quickly and accurately is the main target of this system. Considering the actual requests, this system formulates two modes of aerial route design methods: according to navigating area and two points to design. This paper mainly introduces functions of system and algorithms of aerial route design methods.

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Azevedo, Carlos Lima, João L. Cardoso, Moshe Ben-Akiva, João P. Costeira, and Manuel Marques. "Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing." Procedia - Social and Behavioral Sciences 111 (February 2014): 849–58. http://dx.doi.org/10.1016/j.sbspro.2014.01.119.

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9

Emery, William, and John Schmalzel. "Editorial for “Remote Sensing from Unmanned Aerial Vehicles”." Remote Sensing 10, no. 12 (November 24, 2018): 1877. http://dx.doi.org/10.3390/rs10121877.

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10

Armstrong, Helen Jane, and Jimmie Lundgren. "Cataloging Aerial Photographs and Other Remote-Sensing Materials." Cataloging & Classification Quarterly 27, no. 1-2 (November 19, 1999): 165–227. http://dx.doi.org/10.1300/j104v27n01_08.

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Taragi, R. C. S., K. S. Bisht, and B. S. Sokhi. "Generating population census data through aerial remote sensing." Journal of the Indian Society of Remote Sensing 22, no. 3 (September 1994): 131–38. http://dx.doi.org/10.1007/bf03024774.

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Friedman, Richard A., Anna Sofaer, and Robert S. Weiner. "Remote Sensing of Chaco Roads Revisited." Advances in Archaeological Practice 5, no. 4 (September 12, 2017): 365–81. http://dx.doi.org/10.1017/aap.2017.25.

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ABSTRACTThis paper reports on the first and highly effective use of Light Detection and Ranging (lidar) technology to document Chaco roads, monumental linear surface constructions of the precolumbian culture that occupied the Four Corners region of the American Southwest between approximately AD 600 and 1300. Analysis of aerial photographs supplemented by ground survey has been the traditional methodology employed to identify Chaco roads, but their traces have become increasingly subtle and difficult to detect in recent years due to the impacts of natural weathering, erosion, and land development. Roads that were easily visible in aerial photography and on the ground in the 1980s are now virtually invisible, underscoring the need for new, cutting-edge techniques to detect and document them. Using three case studies of the Aztec Airport Mesa Road, the Great North Road, and the Pueblo Alto Landscape, we demonstrate lidar's unprecedented ability to document known Chaco roads, discover previously undetected road segments, and produce a precise quantitative record of these rapidly vanishing features.

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Melville, W. Kendall, Luc Lenain, Daniel R. Cayan, Mati Kahru, Jan P. Kleissl, P. F. Linden, and Nicholas M. Statom. "The Modular Aerial Sensing System." Journal of Atmospheric and Oceanic Technology 33, no. 6 (June 2016): 1169–84. http://dx.doi.org/10.1175/jtech-d-15-0067.1.

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AbstractSatellite remote sensing has enabled remarkable progress in the ocean, earth, atmospheric, and environmental sciences through its ability to provide global coverage with ever-increasing spatial resolution. While exceptions exist for geostationary ocean color satellites, the temporal coverage of low-Earth-orbiting satellites is not optimal for oceanographic processes that evolve over time scales of hours to days. In hydrology, time scales can range from hours for flash floods, to days for snowfall, to months for the snowmelt into river systems. On even smaller scales, remote sensing of the built environment requires a building-resolving resolution of a few meters or better. For this broad range of phenomena, satellite data need to be supplemented with higher-resolution airborne data that are not tied to the strict schedule of a satellite orbit. To address some of these needs, a novel, portable, high-resolution airborne topographic lidar with video, infrared, and hyperspectral imaging systems was integrated. The system is coupled to a highly accurate GPS-aided inertial measurement unit (GPS IMU), permitting airborne measurements of the sea surface displacement, temperature, and kinematics with swath widths of up to 800 m under the aircraft, and horizontal spatial resolution as low as 0.2 m. These data are used to measure ocean waves, currents, Stokes drift, sea surface height (SSH), ocean transport and dispersion, and biological activity. Hydrological and terrestrial applications include measurements of snow cover and the built environment. This paper describes the system, its performance, and present results from recent oceanographic, hydrological, and terrestrial measurements.

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Li, Qing. "Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Measurement." Remote Sensing 9, no. 2 (October 23, 2020): 42. http://dx.doi.org/10.18282/rs.v9i2.1372.

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<p>Unmanned aerial vehicle remote sensing is widely used in the whole engineering measurement in recent years. It has many advantages including simple operation, high accuracy and high efficiency. It is precisely because of these advantages that unmanned aerial vehicle remote sensing has gradually replaced the traditional surveying and mapping technology to be widely used. With the continuous expansion of the number and scale of projects in China, the effect of unmanned aerial vehicle remote sensing in engineering measurement is getting bigger and bigger. This article mainly analyzes the advantages of unmanned aerial vehicle remote sensing and its application in engineering measurement, so as to provide some reference for the development of surveying and mapping engineering in China.</p>

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Anderson, R. C., P. C. Shanks, L. A. Kritis, and M. G. Trani. "Supporting Remote Sensing Research with Small Unmanned Aerial Systems." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 51–56. http://dx.doi.org/10.5194/isprsarchives-xl-1-51-2014.

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We describe several remote sensing research projects supported with small Unmanned Aerial Systems (sUAS) operated by the NGA Basic and Applied Research Office. These sUAS collections provide data supporting Small Business Innovative Research (SBIR), NGA University Research Initiative (NURI), and Cooperative Research And Development Agreements (CRADA) efforts in addition to inhouse research. Some preliminary results related to 3D electro-optical point clouds are presented, and some research goals discussed. Additional details related to the autonomous operational mode of both our multi-rotor and fixed wing small Unmanned Aerial System (sUAS) platforms are presented.

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Yao, Huang, Rongjun Qin, and Xiaoyu Chen. "Unmanned Aerial Vehicle for Remote Sensing Applications—A Review." Remote Sensing 11, no. 12 (June 18, 2019): 1443. http://dx.doi.org/10.3390/rs11121443.

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The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

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Li, Jianli, Jiancheng Fang, Zhaoxing Lu, and Lijian Bai. "Airborne Position and Orientation System for Aerial Remote Sensing." International Journal of Aerospace Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8721391.

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The airborne Position Orientation System (POS) can accurately measure space-time reference information and plays a vital role in aerial remote sensing system. It may be applied in a direct georeference system for optical camera and a motion imaging system for Synthetic Aperture Radar (SAR), which further advances efficiency and quality of imaging sensors. In this paper, the operation principle and components of airborne POS are introduced. Some key technologies of airborne POS are summarized. They include the error calibration and compensation, initial alignment, lever arm error modeling, time synchronization, and integrated estimation method. A high precision airborne POS has been developed and applied to a variety of aerial remote sensing systems.

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Chen, Shanjing, Chaocan Xiang, Qing Kang, Wei Zhong, Yanlin Zhou, and Kai Liu. "Accurate landslide detection leveraging UAV-based aerial remote sensing." IET Communications 14, no. 15 (September 15, 2020): 2434–41. http://dx.doi.org/10.1049/iet-com.2019.1115.

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19

Franklin, Steven E., and Philip T. Giles. "Radiometric processing of aerial and satellite remote-sensing imagery." Computers & Geosciences 21, no. 3 (April 1995): 413–23. http://dx.doi.org/10.1016/0098-3004(94)00085-9.

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20

Zhao, Ling Li, Shuai Liu, and Li Ma. "Data Acquisition Considering of Fixed-Wing UAVs in Mountainous Areas." Applied Mechanics and Materials 543-547 (March 2014): 2151–54. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2151.

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Over the past decade, there has been a great demand of Unmanned Aerial Vehicles (UAVs) in numerous industrial and military operations around the world. This paper is focused on low fixed-wing UAV remote sensing system, put remote sensing technology and UAV technology closely to fixed-wing unmanned aircraft as a platform, which is equipped with high-resolution digital remote sensing sensors, it has easy transition since the airport does not depend on landing site, it is a new low-speed high-resolution remote sensing data acquisition system. It has capability of a survey of real-time quick monitoring, and has been an effective complement to conventional means for satellite remote sensing and aerial photography.

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21

Amos, E. M., D. Blakeway, and C. D. Warren. "Remote Sensing Techniques in Civil Engineering Surveys." Geological Society, London, Engineering Geology Special Publications 2, no. 1 (1986): 119–24. http://dx.doi.org/10.1144/gsl.1986.002.01.26.

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AbstractThis paper outlines selected remote sensing techniques and their application to civil engineering surveys.In BS 5930, emphasis has been placed on the interpretation of black and white aerial photography to provide information. However, other techniques such as true colour and false colour infrared photography, thermal infrared, radar and landsat satellite imagery may be useful in appropriate applications.

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Anwer, Rao Muhammad, Fahad Shahbaz Khan, and Jorma Laaksonen. "Compact Deep Color Features for Remote Sensing Scene Classification." Neural Processing Letters 53, no. 2 (February 27, 2021): 1523–44. http://dx.doi.org/10.1007/s11063-021-10463-4.

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AbstractAerial scene classification is a challenging problem in understanding high-resolution remote sensing images. Most recent aerial scene classification approaches are based on Convolutional Neural Networks (CNNs). These CNN models are trained on a large amount of labeled data and the de facto practice is to use RGB patches as input to the networks. However, the importance of color within the deep learning framework is yet to be investigated for aerial scene classification. In this work, we investigate the fusion of several deep color models, trained using color representations, for aerial scene classification. We show that combining several deep color models significantly improves the recognition performance compared to using the RGB network alone. This improvement in classification performance is, however, achieved at the cost of a high-dimensional final image representation. We propose to use an information theoretic compression approach to counter this issue, leading to a compact deep color feature set without any significant loss in accuracy. Comprehensive experiments are performed on five remote sensing scene classification benchmarks: UC-Merced with 21 scene classes, WHU-RS19 with 19 scene types, RSSCN7 with 7 categories, AID with 30 aerial scene classes, and NWPU-RESISC45 with 45 categories. Our results clearly demonstrate that the fusion of deep color features always improves the overall classification performance compared to the standard RGB deep features. On the large-scale NWPU-RESISC45 dataset, our deep color features provide a significant absolute gain of 4.3% over the standard RGB deep features.

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Weber, I., A. Jenal, C. Kneer, and J. Bongartz. "GYROCOPTER-BASED REMOTE SENSING PLATFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1333–37. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1333-2015.

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In this paper the development of a lightweight and highly modularized airborne sensor platform for remote sensing applications utilizing a gyrocopter as a carrier platform is described. The current sensor configuration consists of a high resolution DSLR camera for VIS-RGB recordings. As a second sensor modality, a snapshot hyperspectral camera was integrated in the aircraft. Moreover a custom-developed thermal imaging system composed of a VIS-PAN camera and a LWIR-camera is used for aerial recordings in the thermal infrared range. Furthermore another custom-developed highly flexible imaging system for high resolution multispectral image acquisition with up to six spectral bands in the VIS-NIR range is presented. The performance of the overall system was tested during several flights with all sensor modalities and the precalculated demands with respect to spatial resolution and reliability were validated. The collected data sets were georeferenced, georectified, orthorectified and then stitched to mosaics.

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Zhang, Z., W. Feng, T. Wang, Y. Zhang, and L. Ding. "AN IMPROVED AERIAL REMOTE SENSING IMAGE DEFOGGING METHOD BASED ON DARK CHANNEL PRIOR INFORMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 1025–30. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1025-2017.

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Aerial remote sensing image is widely used due to its high resolution, abundant information and convenient processing. However, its image quality is easily influenced by clouds and fog. In recent years, fog and haze air pollution is becoming more and more serious in the north of China and its influence on aerial remote sensing image quality is especially obvious. Considering the characters that aerial remote image is usually in huge amount of data and seldom covers sky area, this paper proposes an improved aerial remote sensing image defogging method based on dark channel prior information. First, a 2&thinsp;% linear stretching is applied to eliminate the haze offset effect and provide a better initial value for later defogging processing. Then the dark channel prior image is obtained by calculating the minimum values of r, g, b channels of each pixel directly. Subsequently, according to the particularity of aerial image, the adaptive threshold t0 is set up to improve the defogging effect. Finally, to improve the color cast phenomenon, a way called automatic color method is introduced to enhance the visual effect of defogged image. Experiments are performed on normal image in fog and on aerial remote sensing image in fog. Experimental results prove that the proposed method can obtain the defogged image with better visual effect and image quality. Moreover, the improved method significantly balances the color information in the defogged image and efficiently avoids the color cast phenomenon.

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Zahul’s’ka, Olha. "Contribution of remote sensing in Ukraine into development of landscape science." Visnyk of the Lviv University. Series Geography, no. 48 (December 23, 2014): 3–11. http://dx.doi.org/10.30970/vgg.2014.48.1287.

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Based on the analysis of publications during the last six years, the main traits of the remote sensing development in Ukraine are analysed. Determined is the contribution of modern information and teaching opportunities of remote sensing to the knowledge about landscapes complexes. Key words: remote sensing, aerial and satellite images, landscape complexes, directions of development of remote sensing (RS) in Ukraine.

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Geng, W., W. Zhou, and S. Jin. "FEATURE FUSION FOR CROSS-MODAL SCENE CLASSIFICATION OF REMOTE SENSING IMAGE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 63–66. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-63-2021.

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Abstract. Scene classification plays an important role in remote sensing field. Traditional approaches use high-resolution remote sensing images as data source to extract powerful features. Although these kind of methods are common, the model performance is severely affected by the image quality of the dataset, and the single modal (source) of images tend to cause the mission of some scene semantic information, which eventually degrade the classification accuracy. Nowadays, multi-modal remote sensing data become easy to obtain since the development of remote sensing technology. How to carry out scene classification of cross-modal data has become an interesting topic in the field. To solve the above problems, this paper proposes using feature fusion for cross-modal scene classification of remote sensing image, i.e., aerial and ground street view images, expecting to use the advantages of aerial images and ground street view data to complement each other. Our cross- modal model is based on Siamese Network. Specifically, we first train the cross-modal model by pairing different sources of data with aerial image and ground data. Then, the trained model is used to extract the deep features of the aerial and ground image pair, and the features of the two perspectives are fused to train a SVM classifier for scene classification. Our approach has been demonstrated using two public benchmark datasets, AiRound and CV-BrCT. The preliminary results show that the proposed method achieves state-of-the-art performance compared with the traditional methods, indicating that the information from ground data can contribute to aerial image classification.

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Chen, Rong. "Application of UAV-Low Altitude Remote Sensing System in Sea Area Supervision." Earth Sciences Research Journal 25, no. 1 (April 16, 2021): 65–68. http://dx.doi.org/10.15446/esrj.v25n1.94162.

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The sea area supervision is the premise and guarantee of safeguarding national security, protecting national sovereignty, and realizing the development of marine resources, and its importance is self-evident. To carry out the national sea area work more efficiently, this study designed low altitude-Unmanned Aerial Vehicles (UAV) remote sensing system applied to the sea area supervision and analyzed the remote sensing photography technology and remote sensing image processing technology. Experiments verified the effectiveness of the system. The research results show that the UAV-based low altitude remote sensing system can extract high-precision sea area information through aerial images’ interpretation. It is hoped that this study can provide some reference for improving the efficiency of current sea area supervision.

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Hardin, Perry J., and Ryan R. Jensen. "Introduction—Small-Scale Unmanned Aerial Systems for Environmental Remote Sensing." GIScience & Remote Sensing 48, no. 1 (January 2011): 1–3. http://dx.doi.org/10.2747/1548-1603.48.1.1.

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Mastelic, Toni, Josip Lorincz, Ivan Ivandic, and Matea Boban. "Aerial Imagery Based on Commercial Flights as Remote Sensing Platform." Sensors 20, no. 6 (March 17, 2020): 1658. http://dx.doi.org/10.3390/s20061658.

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Remote sensing is commonly performed via airborne platforms such as satellites, specialized aircraft, and unmanned aerial systems (UASs), which perform airborne photography using mounted cameras. However, they are limited by their coverage (UASs), irregular flyover frequency (aircraft), and/or low spatial resolution (satellites) due to their high altitude. In this paper, we examine the utilization of commercial flights as an airborne platform for remote sensing. Namely, we simulate a situation where all aircraft on commercial flights are equipped with a mounted camera used for airborne photography. The simulation is used to estimate coverage, the temporal and spatial resolution of aerial imagery acquired this way, as well as the storage capacity required for storing all imagery data. The results show that Europe is 83.28 percent covered with an average of one aerial photography every half an hour and a ground sampling distance of 0.96 meters per pixel. Capturing such imagery results in 20 million images or four petabytes of image data per day. More detailed results are given in the paper for separate countries/territories in Europe, individual commercial airlines and alliances, as well as three different cameras.

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Olejnik, Aleksander, Łukasz Kiszkowiak, Robert Rogólski, Grzegorz Chmaj, Michał Radomski, Maciej Majcher, and Łukasz Omen. "The Use of Unmanned Aerial Vehicles in Remote Sensing Systems." Sensors 20, no. 7 (April 3, 2020): 2003. http://dx.doi.org/10.3390/s20072003.

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This paper describes the possibility of using a small autonomous helicopter to perform tasks using a remote sensing system. This article further shows the most effective way to properly set up autopilot and to process its validation during flight tests. The most important components of the remote sensing system are described and the possibilities of using this system to monitor gas transmission and distribution networks are presented.

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Dando, R. C. A. "ROYAL PHOTOGRAPHIC SOCIETY ACTIVITIES IN AERIAL PHOTOGRAPHY AND REMOTE SENSING." Photogrammetric Record 14, no. 79 (August 26, 2006): 129–30. http://dx.doi.org/10.1111/j.1477-9730.1992.tb00212.x.

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Ji, Shunping, Jin Liu, and Meng Lu. "CNN-Based Dense Image Matching for Aerial Remote Sensing Images." Photogrammetric Engineering & Remote Sensing 85, no. 6 (June 1, 2019): 415–24. http://dx.doi.org/10.14358/pers.85.6.415.

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Colomina, I., and P. Molina. "Unmanned aerial systems for photogrammetry and remote sensing: A review." ISPRS Journal of Photogrammetry and Remote Sensing 92 (June 2014): 79–97. http://dx.doi.org/10.1016/j.isprsjprs.2014.02.013.

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Stark, Brandon, Calvin Coopmans, and YangQuan Chen. "Concept of Operations for Personal Remote Sensing Unmanned Aerial Systems." Journal of Intelligent & Robotic Systems 69, no. 1-4 (August 2, 2012): 5–20. http://dx.doi.org/10.1007/s10846-012-9710-9.

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Tian, Yanqin, Chenghai Yang, Wenjiang Huang, Jia Tang, Xingrong Li, and Qing Zhang. "Machine learning-based crop recognition from aerial remote sensing imagery." Frontiers of Earth Science 15, no. 1 (March 2021): 54–69. http://dx.doi.org/10.1007/s11707-020-0861-x.

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Sassu, Alberto, Filippo Gambella, Luca Ghiani, Luca Mercenaro, Maria Caria, and Antonio Luigi Pazzona. "Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture." Sensors 21, no. 3 (February 1, 2021): 956. http://dx.doi.org/10.3390/s21030956.

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New technologies for management, monitoring, and control of spatio-temporal crop variability in precision viticulture scenarios are numerous. Remote sensing relies on sensors able to provide useful data for the improvement of management efficiency and the optimization of inputs. unmanned aerial systems (UASs) are the newest and most versatile tools, characterized by high precision and accuracy, flexibility, and low operating costs. The work aims at providing a complete overview of the application of UASs in precision viticulture, focusing on the different application purposes, the applied equipment, the potential of technologies combined with UASs for identifying vineyards’ variability. The review discusses the potential of UASs in viticulture by distinguishing five areas of application: rows segmentation and crop features detection techniques; vineyard variability monitoring; estimation of row area and volume; disease detection; vigor and prescription maps creation. Technological innovation and low purchase costs make UASs the core tools for decision support in the customary use by winegrowers. The ability of the systems to respond to the current demands for the acquisition of digital technologies in agricultural fields makes UASs a candidate to play an increasingly important role in future scenarios of viticulture application.

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Thelen, Kurt D., A. N. Kravchenko, and Chad D. Lee. "Use of Optical Remote Sensing for Detecting Herbicide Injury in Soybean." Weed Technology 18, no. 2 (June 2004): 292–97. http://dx.doi.org/10.1614/wt-03-049r2.

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Experiments were conducted from 2000 to 2002 at two locations each year to determine if lactofen and imazethapyr injury to soybean could be detected using digital aerial imagery and ground-based optical remote sensing. Lactofen and imazethapyr were applied at base rates of 105 and 71 g/ha, respectively, and at 0, 2X, and 4X rates. Treated plots were evaluated between 7 and 21 d after treatment for crop injury using a ground-based radiometer and a system using computer analysis of digital aerial imagery. Both the ground-based radiometer and the digital aerial imagery were effective in detecting herbicide injury under most conditions. The digital aerial imagery system was found to be more sensitive in detecting herbicide injury than the ground-based radiometer system. Herbicide or herbicide rate had a significant effect on normalized differential vegetation indices (NDVI) derived from digital aerial imagery in four of four site-years. NDVI values derived from a multispectral ground-based radiometer were significant for herbicide or herbicide rate in four of six site-years. NDVI values from treated plots were subtracted from the NDVI value of the untreated check to generate a ΔNDVI. The resulting ΔNDVI values from the ground-based radiometer system were significant for herbicide or herbicide rate in six of six site-years. Neither optical remote-sensing system was effective at estimating actual application rates of lactofen and imazethapyr across a broad range of field and weather conditions due to temporal and spatial variability in crop response to the herbicides.

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da Silva, C. C. V., K. Nogueira, H. N. Oliveira, and J. A. dos Santos. "TOWARDS OPEN-SET SEMANTIC SEGMENTATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3/W2-2020 (October 29, 2020): 19–24. http://dx.doi.org/10.5194/isprs-annals-iv-3-w2-2020-19-2020.

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Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increasing mainly due to the low cost of drones, cheapening of imaging satellite launch costs, and novel public datasets. Usually remote sensing applications employ computer vision techniques strictly modeled for classification tasks in closed set scenarios. However, real-world tasks rarely fit into closed set contexts, frequently presenting previously unknown classes, characterizing them as open set scenarios. Focusing on this problem, this is the first paper to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of a novel approach for open set semantic segmentation. Our method yielded competitive results when compared to closed set methods for the same dataset.

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Albrecht, Conrad M., Rui Zhang, Xiaodong Cui, Marcus Freitag, Hendrik F. Hamann, Levente J. Klein, Ulrich Finkler, et al. "Change Detection from Remote Sensing to Guide OpenStreetMap Labeling." ISPRS International Journal of Geo-Information 9, no. 7 (July 2, 2020): 427. http://dx.doi.org/10.3390/ijgi9070427.

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The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.

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Themistocleous, K., A. Agapiou, B. Cuca, and D. G. Hadjimitsis. "Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1419–23. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1419-2015.

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Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In- situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.

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Dong, Li Na, Jing Tong, and Chen Yang Wang. "The Application of Airborne Remote Sensing Technology in Land and Resources." Applied Mechanics and Materials 644-650 (September 2014): 4360–63. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4360.

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Airborne and space remote sensing system are all the important parts of the earth observation system, also being good supplements to each other. Airborne remote sensing has the advantages of being high resolution, good efficiency and flexibility, which makes itself an effective method to rapidly acquire high resolution remote sensing data. Particularly, the technologies of conducting low altitude remote sensing investigation by unmanned aerial vehicles are rapidly developed with a great progress achieved, so there is no doubt that it will plays an important role in the remote sensing geological investigation.

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Bidolah, D. I. "Geoinformation monitoring of green stands using remote sensing methods." Ukrainian Journal of Forest and Wood Science 11, no. 2 (June 30, 2020): 4–14. http://dx.doi.org/10.31548/forest2020.02.004.

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A green plantations monitoring is an important work, which includes regular monitoring of urban landscapes in order to identify negative timely changes and to prepare the informed decisions to prevent their degradation. In modern conditions, it is advisable to improve this process by using remote sensing methods, the materials of which are processed in geographic information systems, which allows to establish an automated monitoring system of green areas. The purpose of the study is to substantiate the feasibility of geo-information monitoring of urban landscapes using remote sensing methods, including unmanned aerial vehicles. To achieve this goal, structural schemes of the monitoring system organization to get the information about green plantation conditions are proposed, together with the possibilities of using orthophotoplans, which are obtained by remote sensing methods for the needs of regular study of urban landscapes. Repeated remote monitoring of the green plantations condition allows to detect the timely changes that have occurred with tree and shrub vegetation over a period in an automated mode. The use of remote sensing materials in this case can be used as a documentary basis to justify the implementation of measures for landscaping. Due to the expensive cost of high-resolution materials, we recommend to use in the process of urban landscape monitoring proposed approach with regular surveys of unmanned aerial vehicles for greenery facilities. On the basis of the conducted researches the multifunctional structure of the green plantings monitoring system is offered and carrying out of digital transformation of monitoring process of urban landscapes condition is recommended. These studies indicate the feasibility of introducing regular automated monitoring of green areas with the involvement of remote sensing methods, including using unmanned aerial vehicles.

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Yin Pengfei, 尹鹏飞, 尹球 Yin Qiu, 陈兴峰 Chen Xingfeng, 许华 Xu Hua, and 张文君 Zhang Wenjun. "Unmanned Aerial Vehicle Aerial Remote Sensing Techniques and Its Application on Post-Earthquake Disaster Investigation." Laser & Optoelectronics Progress 47, no. 11 (2010): 112802. http://dx.doi.org/10.3788/lop47.112802.

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Hall, R. J., G. Castilla, J. C. White, B. J. Cooke, and R. S. Skakun. "Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective." Canadian Entomologist 148, S1 (May 12, 2016): S296—S356. http://dx.doi.org/10.4039/tce.2016.11.

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AbstractOutbreaks of insect pests periodically cause large losses of volume in Canada’s forests. Compounded with climate change, outbreaks create significant challenges for managing the sustainable delivery of ecosystem services. Current methods to monitor damage by these pests involve both field and aerial surveys. While relatively cost effective and timely, aerial survey consistency and spatial coverage may be insufficient for detailed monitoring across Canada’s vast forest-land base. Remote sensing can augment these methods and extend monitoring capabilities in time and space by incorporating knowledge of pest-host interactions and of how damage translates into a remote sensing signal for detection and mapping. This review provides a brief introduction to major forest insect pests in Canada (two bark beetles (Coleoptera: Curculionidae) and six defoliators) and the damage they cause, a synthesis of the literature involving aerial survey and remote sensing, and a discussion of how these two approaches could be integrated into future pest monitoring from a Canadian perspective. We offer some lessons learned, outline roles that remote sensing could serve in a management context, and discuss what ongoing and upcoming technological advances may offer to future forest health monitoring.

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Bennett, Rohan, Peter van Oosterom, Christiaan Lemmen, and Mila Koeva. "Remote Sensing for Land Administration." Remote Sensing 12, no. 15 (August 4, 2020): 2497. http://dx.doi.org/10.3390/rs12152497.

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Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.

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JIANG, Xin, Wu-xiong CHEN, Hai-tao NIE, and Zhi-cheng HAO. "Real-time ship target detection based on aerial remote sensing images." Optics and Precision Engineering 28, no. 10 (2020): 2360–69. http://dx.doi.org/10.37188/ope.20202810.2360.

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Chen, L., J. Hu, G. Lv, and Y. Liu. "SPACE-AERIAL-GROUND INTEGRATED CITY DYNAMIC MONITORING BASED ON REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 29–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-29-2020.

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Abstract. With the further acceleration of the new urbanization process, China's urbanization construction has entered a new stage of more standardized and scientific development, which puts forward higher requirements for national land and space monitoring. In view of the rapid changes in looks of our cities, the periodic monitoring methods such as personnel patrolling inspection, public reporting or satellite remote sensing are difficult to meet the needs of full-range, dynamic and precise monitoring. Therefore, it is urgent to establish a new spatial monitoring system to make up for the deficiencies of current urban spatial monitoring. In the article, the authors provide an implementation idea of a space-aerial-ground integrated remote sensing monitoring method system, which integrates various remote sensing techniques and information platform to meet the needs of large-scale urban change cycle monitoring and continuous monitoring of key construction areas Space monitoring provides modern technical support. Some of the techniques in the method system described here have been applied in some regions, which provides a basic platform for regional city monitoring, and have further research and application prospects.

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Hong, Z. "THE DEVELOPMENT AND INDUSTRIALIZATION RECOMMENDATION OF CURRENT AERIAL REMOTE SENSING TECHNOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W1 (July 12, 2013): 37–39. http://dx.doi.org/10.5194/isprsarchives-xl-7-w1-37-2013.

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Klemas, Victor V. "Coastal and Environmental Remote Sensing from Unmanned Aerial Vehicles: An Overview." Journal of Coastal Research 315 (September 2, 2015): 1260–67. http://dx.doi.org/10.2112/jcoastres-d-15-00005.1.

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Zhao, Yu, Fan Feng Meng, and Jiang Feng. "Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods." Advanced Materials Research 905 (April 2014): 585–88. http://dx.doi.org/10.4028/www.scientific.net/amr.905.585.

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In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discussed.

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