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Relevant bibliographies by topics / Remote sensing - video

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Academic literature on the topic 'Remote sensing - video'

Author: Grafiati

Published: 30 November 2024

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Journal articles on the topic "Remote sensing - video"

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Ye, Fanghong, Tinghua Ai, Jiaming Wang, Yuan Yao, and Zheng Zhou. "A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data." Remote Sensing 14, no. 10 (May 11, 2022): 2324. http://dx.doi.org/10.3390/rs14102324.

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The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.

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MEISNER, D. "Fundamentals of airborne video remote sensing☆." Remote Sensing of Environment 19, no. 1 (February 1986): 63–79. http://dx.doi.org/10.1016/0034-4257(86)90041-6.

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Su, Zhijuan, Gang Wan, Wenhua Zhang, Ningbo Guo, Yitian Wu, Jia Liu, Dianwei Cong, Yutong Jia, and Zhanji Wei. "An Integrated Detection and Multi-Object Tracking Pipeline for Satellite Video Analysis of Maritime and Aerial Objects." Remote Sensing 16, no. 4 (February 19, 2024): 724. http://dx.doi.org/10.3390/rs16040724.

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Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural network) is proposed for optical remote sensing videos based on a correlation filter and deep neural networks. The pipeline is used to simultaneously track ships and planes in videos. There are many target tracking methods for general video data, but they suffer some difficulties in remote sensing videos with low resolution and those influenced by weather conditions. The tracked targets are usually misty. Therefore, in TDNet, we propose a new multi-target tracking method called MT-KCF and a detecting-assisted tracking (i.e., DAT) module to improve tracking accuracy and precision. Meanwhile, we also design a new target recognition (i.e., NTR) module to recognise newly emerged targets. In order to verify the performance of TDNet, we compare our method with several state-of-the-art tracking methods on optical video remote sensing data sets acquired from the Jilin No. 1 satellite. The experimental results demonstrate the effectiveness and the state-of-the-art performance of the proposed method. The proposed method can achieve more than 90% performance in terms of precision for single-target tracking tasks and more than 85% performance in terms of MOTA for multi-object tracking tasks.

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Wang, Y., H. Cheng, X. Zhou, W. Luo, and H. Zhang. "MOVING SHIP DETECTION AND MOVEMENT PREDICTION IN REMOTE SENSING VIDEOS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1303–8. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1303-2020.

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Abstract. With the rapid development of remote sensing technology, it is possible to obtain continuous video data from outer space successfully. It is of great significance in military and civilian fields to detect moving objects from the remote sensing image sequence and predict their movements. In recent years, this issue has attracted more and more attention. However, researches on moving object detection and movement prediction in high-resolution remote sensing videos are still in its infancy, which is worthy of further study. In this paper, we propose a ship detection and movement prediction method based on You-Only-Look-Once (YOLO) v3 and Simple Online and Realtime Tracking (SORT). Original YOLO v3 is improved by multi-frame training to fully utilize the information of continuous frames in a fusion way. The simple and practical multiple object tracking algorithm SORT is used to recognize multiple targets detected by multi-frame YOLO v3 model and obtain their coordinates. These coordinates are fitted by the least square method to get the trajectories of multiple targets. We take the derivative of each trajectory to obtain the real-time movement direction and velocity of the detected ships. Experiments are performed on multi-spectral remote sensing images selected on Google Earth, as well as real multi-spectral remote sensing videos captured by Jilin-1 satellite. Experimental results validate the effectiveness of our method for moving ship detection and movement prediction. It shows a feasible way for efficient interpretation and information extraction of new remote sensing video data.

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Sun, Siqiu, and Tianbo Xiong. "Application of Remote Sensing Technology in Sustainable Urban Planning and Development." Applied and Computational Engineering 3, no. 1 (May 25, 2023): 283–88. http://dx.doi.org/10.54254/2755-2721/3/20230475.

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Urban planning is super vital for cities. Good planning for the city would provide more convenience and benefits to citizens through economic effects, beautiful scenery and so on. This study analyses the application of remote sensing technologies in urban landscaping planning, transportation, and environmental protection. The combination of remote sensing and GIS technologies makes urban landscaping planning more accessible in urban landscaping planning. Usually, remote sensing provides satellite images, while the GIS processes the image and compares the target region for analysis. The combination method could predict urban green spaces. In urban transportation planning, remote sensing technology could combine with Artificial intelligence smart video technology to protect peoples safety in transportation, such as observing traffic violations by capturing videos or images and protecting traffic safety. In environmental protection, remote sensing could monitor the area change of wetlands and solve the natural disasters around the city. Different approaches have their benefits and disadvantages, and thus making urban plans by combining various techniques is essential.

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Baldock, Tom E., Theo Moura, and Hannah E. Power. "Video-Based Remote Sensing of Surf Zone Conditions." IEEE Potentials 36, no. 2 (March 2017): 35–41. http://dx.doi.org/10.1109/mpot.2016.2631018.

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KANG, Jinzhong, Guizhou WANG, Guojin HE, Huihui WANG, Ranyu YIN, Wei JIANG, and Zhaoming ZHANG. "Moving vehicle detection for remote sensing satellite video." National Remote Sensing Bulletin 24, no. 9 (2020): 1099–107. http://dx.doi.org/10.11834/jrs.20208364.

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Lei, Lei, and Dongen Guo. "Multitarget Detection and Tracking Method in Remote Sensing Satellite Video." Computational Intelligence and Neuroscience 2021 (August 31, 2021): 1–7. http://dx.doi.org/10.1155/2021/7381909.

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A remote sensing video satellite multiple object detection and tracking method based on road masking, Gaussian mixture model (GMM), and data association is proposed. This method first extracts the road network from the remote sensing video based on deep learning. In the detection stage, the background subtraction algorithm is used based on the GMM to obtain the detection results of the moving targets on the road. In the tracking stage, the data association of the same target detection result in adjacent frames is realized based on the neighborhood search algorithm, so as to obtain the continuous tracking trajectory of each target. The experiments about multiobject detection and tracking are conducted on data measure by real remote sensing satellites, and the results verified the feasibility of the proposed method.

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Everitt, James H., David E. Escobar, Mario A. Alaniz, Ricardo Villarreal, and Michael R. Davis. "Distinguishing Brush and Weeds on Rangelands Using Video Remote Sensing." Weed Technology 6, no. 4 (December 1992): 913–21. http://dx.doi.org/10.1017/s0890037x00036472.

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This paper describes the application of a relatively new remote sensing tool, airborne video imagery, for distinguishing weed and brush species on rangelands. Plant species studied were false broomweed, spiny aster, and Chinese tamarisk. A multispectral video system that acquired color-infrared (CIR) composite imagery and its simultaneously synchronized three-band [near-infrared (NIR), red, and yellow-green] narrowband images was used for the false broomweed and spiny aster experiments. A conventional color camcorder video system was used to study Chinese tamarisk. False broomweed and spiny aster could be detected on CIR composite and NIR narrowband imagery, while Chinese tamarisk could be distinguished on conventional color imagery. Quantitative data obtained from digitized video images of the three species showed that their digital values were statistically different (P = 0.05) from those of associated vegetation and soil. Computer analyses of video images showed that populations of the three species could be quantified from associated vegetation. This technique permits area estimates of false broomweed, spiny aster, and Chinese tamarisk populations on rangeland and wildland areas.

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Wu, Yiguang, Meizhen Wang, Xuejun Liu, Ziran Wang, Tianwu Ma, Zhimin Lu, Dan Liu, Yujia Xie, Xiuquan Li, and Xing Wang. "Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing." Remote Sensing 13, no. 19 (September 26, 2021): 3853. http://dx.doi.org/10.3390/rs13193853.

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Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

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Dissertations / Theses on the topic "Remote sensing - video"

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Buyuksalih, Gurcan. "Geometric and radiometric calibration of video infrared imagers for photogrammetric applications." Thesis, University of Glasgow, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284703.

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Lee, Christopher Tom. "The use of multispectral video remote sensing to monitor suspended sediment concentrations." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185263.

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Detailed knowledge of suspended sediment concentration and sediment transport rates is one of the most important and, most elusive goals of data collection for studies involving stream channel morphology and water quality monitoring. Reliable, continuous data on the suspended sediment load of streams are virtually nonexistent. Although it is relatively easy to obtain information on the water discharge through an open channel, it is far more difficult to sample the sediment load of a stream. Suspended sediment concentration is highly variable with time, cumbersome and costly to sample even for short duration periods, and largely independent of other stream flow characteristics such as discharge. This study explored the potential of using ground-based multispectral video remote sensing to monitor suspended sediment concentrations. Data were collected during seven sampling episodes at two test sites in northern Arizona. Two different video systems were employed. The Xybion system recorded reflectance data in four bandwidths for three dates while the Bi-spectral system recorded data in two spectral bands for the remaining four episodes. Video imagery was acquired simultaneously with water samples containing suspended sediment. Digital numbers (DNs) for the two and four band imagery were extracted from 5 by 5 pixel arrays and the average value computed. Water samples were analyzed to determine sediment concentration. Relationships between DNs and reflectance were explored through the use of simple and multiple linear and non-linear regression. Models were generated for the best single and multiple band combinations for both the single date and multidate data sets. Correlation coefficients for the models were comparable or superior to similar models generated from orbital and airborne data. The results of the study indicate that the ground-based technique has great promise for providing suspended sediment data of high spatial and temporal resolution to supplement existing measurement techniques. Recommendations for future work to help improve the basic technique are included.

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Meyer, Jill E. "Use of a Digital Multispectral Video System and Spectroradiometer for Bottomland Hardwood Forest Remote Sensing: A Jurisdictional Boundary Accuracy Assessment and Radiance Examination." W&M ScholarWorks, 2000. https://scholarworks.wm.edu/etd/1539617758.

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Nolin, Anne Walden 1958. "CLASSIFICATION AND MAPPING OF SOILS USING A MULTISPECTRAL VIDEO SYSTEM AND COMPUTER-AIDED ANALYSIS." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276549.

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An airborne multispectral video system was used to collect soil spectral data over a four-square mile region in northeastern Arizona. Six multispectral video images were digitized. Using the red and blue bands of each image, an unsupervised classification was performed. Each was referenced to a digitized U.S. Soil Conservation Service map resulting in classification precisions ranging from 0-92.4 percent. Ground radiometric measurements were made to ascertain spectral separability of the soil samples. Soil color was determined to try to relate Munsell value to classification precision. Misclassification of soil map units was unrelated to soil brightness or areal extent of each soil. Rather, features such as slope, boundary complexity, and surface condition was responsible for misclassifications seen in this study. Best classification results occurred when soil mapping units were relatively homogeneous, possessed slight changes in slope, and had a regular surface with smooth and distinct boundaries.

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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 classification was used as input to a runoff simulation model. Runoff values generate by each simulation were compared to observed runoff. A chi-square goodness-of-fit test indicated that SPOT produced landcover data most similar to the conventional classification. In the curve number model, the SPOT data produced simulated runoff values most similar to observed runoff.

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PERUGINI, ELEONORA. "The Application of Video-Monitoring Data to Understand Coastal and Estuarine Processes." Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263695.

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La presente tesi riguarda l’uso dei dati raccolti da una nuova stazione di video monitoraggio, denominata SGS allo scopo di accrescere la conoscenza dei processi idro-morfodinamici caratteristici di una spiaggia sabbiosa naturale tipica della costa Adriatica, in prossimità di un estuario. La stazione è stata installata all’interno del porto di Senigallia e ogni ora registra un video di dieci minuti a 2Hz. In questa tesi, sono stati elaborati i video raccolti dal 2015 al 2017. Le immagini derivanti sono state utilizzate in due differenti analisi: 1) lo studio della capacità di stimare la batimetria applicando il codice cBathy (un algoritmo ampiamente utilizzato per la valutazione della profondità dell’acqua tramite analisi inversa) ai dati ottici provenienti dalla stazione e 2) lo studio della dinamica di un sistema di barre sommerse. La stima della profondità dell’acqua varia, in termini di qualità, in funzione della posizione spaziale e delle condizioni ondose e si è osservata una generale sottostima della profondità in gran parte del dominio. Si è quindi eseguita una dettagliata analisi per comprendere il motivo di tale risultato. La causa principale è stata attribuita al grande angolo presente tra l’asse ottico della telecamera e la direzione di propagazione delle onde incidenti. Dei test sintetici sono stati usati per analizzare più in profondità questo aspetto. La procedura utilizzata può essere applicata anche per il progetto di nuove stazioni di video-monitoraggio. Per quanto riguarda la seconda analisi, le immagini disponibili permettono di ben identificare la complessa variabilità 3D (commutazione e biforcazione) del sistema di barre sommerse, sottolineando la capacità della stazione SGS di monitorare le caratteristiche del fondale dell’area oggetto di studio. Tre principali ordini di barre sono stati identificati in accordo con gli studi precedenti basati su dati raccolti da indagini in situ e la loro migrazione è stata correlata con il clima ondoso. Durante il periodo analizzato, è stata riscontrata una generale stabilità del sistema di barre in risposta a eventi di tempesta con onde provenienti da NNE, mentre è stato osservato un netto movimento delle barre verso il largo durante l’unica tempesta con onde provenienti da ESE. Il diverso comportamento è stato collegato alla diversa orientazione delle onde e alla riflessione delle onde provenienti da ESE dal molo del fiume.
The present thesis concerns the application of the data coming from a new video-monitoring station, called SGS, to improve knowledge of the hydro-morphodynamic processes on a typical natural sandy beach near an estuary along the Adriatic coast (Italy). The SGS station was installed in the Senigallia harbour and collects ten minutes of full-frame images at 2Hz each hour. In this work, the videos of the period 2015-2017 have been post-processed. The elaborated images have been used to perform two different analyses: 1) the study of the capability of the SGS data to estimate the water depth using cBathy (a widely used algorithm for depth-inversion) and 2) the study of the dynamics of a multiple sandbars system. The results of the first analysis varied in quality as a function of the location and wave conditions and a general underestimation of the depth has been found in a large portion of the domain. A detailed debugging analysis was carried out to find the reasons of this poor performance. The main source of error was found to be the large angle between the camera viewing direction and the direction of propagation of the incident waves. A synthetic analysis was performed to analyse in depth this aspect. The synthetic procedure can be applied also to design future shore-based video monitoring stations. With reference to the second analysis, the complex 3D-variability (switching and bifurcation) of the multiple sandbars system has been recognized from the images, this highlighting the capability of the SGS station to monitor the seabed features of the study field. Three orders of bars have been identified, in agreement with previous studies based on data collected by in-situ surveys and their motion has been correlated with the wave climate. In the analysed period, a general stability of the bar system in response to storm events with waves coming from NNE has been found, while a net offshore migration has been observed under the only storm with waves coming from ESE. The different behaviour has been related to the wave direction and to the reflection of the ESE waves off the wall of the nearby river pier.

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Garges, David Casimir. "Early Forest Fire Detection via Principal Component Analysis of Spectral and Temporal Smoke Signature." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1456.

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The goal of this study is to develop a smoke detecting algorithm using digital image processing techniques on multi-spectral (visible & infrared) video. By utilizing principal component analysis (PCA) followed by spatial filtering of principal component images the location of smoke can be accurately identified over a period of exposure time with a given frame capture rate. This result can be further analyzed with consideration of wind factor and fire detection range to determine if a fire is present within a scene. Infrared spectral data is shown to contribute little information concerning the smoke signature. Moreover, finalized processing techniques are focused on the blue spectral band as it is furthest away from the infrared spectral bands and because it experimentally yields the largest footprint in the processed principal component images in comparison to other spectral bands. A frame rate of .5 images/sec (1 image every 2 seconds) is determined to be the maximum such that temporal variance of smoke can be captured. The study also shows eigenvectors corresponding to the principal components that best represent smoke and are valuable indications of smoke temporal signature. Raw video data is taken through rigorous pre-processing schemes to align frames from respective spectral band both spatially and temporally. A multi-paradigm numerical computing program, MATLAB, is used to match the field of view across five spectral bands: Red, Green, Blue, Long-Wave Infrared, and Mid-Wave Infrared. Extracted frames are aligned temporally from key frames throughout the data capture. This alignment allows for more accurate digital processing for smoke signature. v Clustering analysis on RGB and HSV value systems reveal that color alone is not helpful to segment smoke. The feature values of trees and other false positives are shown to be too closely related to features of smoke for in solely one instance in time. A temporal principal component transform on the blue spectral band eliminates static false positives and emphasizes the temporal variance of moving smoke in images with higher order. A threshold adjustment is applied to a blurred blue principal component of non-unity principal component order and smoke results can be finalized using median filtering. These same processing techniques are applied to difference images as a more simple and traditional technique for identifying temporal variance and results are compared.

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Yoo, Jeseon. "Nonlinear Bathymetry Inversion Based on Wave Property Estimation from Nearshore Video Imagery." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19793.

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Video based remote sensing techniques are well suited to collect spatially resolved wave images in the surf zone with breaking waves and dynamic bathymetric changes. An advanced video-based depth inversion method is developed to remotely survey bathymetry in the surf zone. The present method involves image processing of original wave image sequences, wave property estimation based on linear feature extraction from the processed image sequences, and is combined with a nonlinear depth inversion model. The original wave image sequences are processed through video image frame differencing and directional low-pass filtering schemes to remove wave-breaking-induced foam noise having high frequencies in the surf zone. The features of individual crest trajectories are extracted from the processed and rectified image sequences, i.e. processed image cross-shore timestacks, by tracking pixels of high intensity within an interrogation window of a Radon-transform-based line-detection algorithm. The wave celerity is computed using space-time information of the extracted trajectories of individual wave crests in the cross-shore timestack domain. The presented retrieval of nearshore bathymetry from video image sequences is based on a nonlinear depth inversion using the nonlinear shallow water wave theory. The nonlinear wave amplitude dispersion effects at the breaker points are determined by combining the nonlinear shallow water celerity equation with a wave breaker criterion, thereby computing water depths iteratively from the celerity measured from the video data. The water depths estimated at the breaker points present initial bathymetric anchor points. Bathymetric profiles in the surf zone are inverted by calculating wave heights dissipated after wave breaking with a wave dissipation model and wave heights shoaled before wave breaking with a wave shoaling model. The continuous wave amplitude dispersion effects are subtracted from the measured celerity profiles, resulting in nearshore bathymetric profiles. The nonlinear depth inversion derived bathymetric estimates from nearshore imagery match the measured values with a biased mean depth error of about +0.06m in the depth range of 0.1 to 3m. In addition, the wave height estimates by the depth inversion model are comparable to the in-situ measured wave heights with a biased mean wave height error of about +0.14m. The present depth inversion method based on optical remote-sensing supports coastal management, navigation, and amphibious operations.

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Salva, Karol T. "A Hybrid Approach to Aerial Video Image Registration." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1483524722687971.

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Machabée, Jaimie. "Caractérisation de l'hydro-morphodynamique de la zone intertidale du récif frangeant de l'Hermitage par caméra vidéo (La Réunion)." Electronic Thesis or Diss., La Réunion, 2024. http://www.theses.fr/2024LARE0015.

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Les récifs coralliens, environnements emblématiques de la biodiversité mondiale, font face aux défis posés par le changement climatique, l'élévation du niveau de la mer et l'anthropisation côtière croissante. Les plages adossées à ces récifs, en particulier les plages d'arrière-récifs frangeants, restent relativement peu étudiées malgré leur importance écologique et économique. Ce manuscrit relate les travaux de thèse visant à améliorer notre compréhension de l'hydro-morphodynamique de ces plages à différentes échelles temporelles, en utilisant une caméra vidéo (Video Monitoring System, VMS) couplée à une sonde de pression. Le site d'étude, la plage de l'Hermitage, située dans une zone microtidale sur la côte ouest de l'île de La Réunion dans l'océan Indien, est fréquemment exposée aux houles australes et aux événements cycloniques. Cette plage est suivie par DGPS et drone dans le cadre du Système National d'Observation Dynalit (SNO Dynalit) depuis 2014. Les constats depuis 1950 et les observations actuelles montrent une plage fortement érodée, conséquence de plusieurs facteurs anthropiques. Les résultats de ce travail de thèse montrent la pertinence et les limites de la mobilisation d'une telle instrumentation (caméra vidéo et sonde de pression) pour l'étude des plages d'arrière-récif. Une cyclicité saisonnière de la position de la ligne d'eau (ou ligne de rivage), avec un recul de 1,4 m en hiver et une avancée de 0,9 m en été, est observée. En moyenne, le récif atténue 97% des houles incidentes et agit comme un filtre passe-bas. Par ailleurs, une analyse du runup sur l'estran révèle que les ondes de basses fréquences contribuent à 50% à ce phénomène, et le setup à 40%. Notre étude montre le rôle des ondes longues (infragravitaires (IG) et Very Low Frequency (VLF)) dans les phénomènes de submersion mais aussi d'érosion. Ce travail ouvre des perspectives importantes pour la recherche dans ce type d'environnement dans un contexte de changement climatique et d'élévation du niveau de la mer
Coral reefs, iconic environments of global biodiversity, face the challenges of climate change, rising sea levels, and increasing coastal development. Despite their ecological and economic significance, beaches adjacent to these reefs, especially fringing back-reef beaches, have received relatively little attention. This manuscript presents doctoral research aimed at enhancing our understanding of the hydro-morphodynamics of these beaches across various time scales, utilizing a Video Monitoring System (VMS) coupled with a pressure sensor. The study site, Hermitage Beach, situated in a microtidal zone on the western coast of La Réunion in the Indian Ocean, is frequently exposed to southern swells and cyclonic events. Since 2014, this beach has been monitored by Differential Global Positioning System (DGPS) and drone as part of the Dynalit National Observation System (SNO Dynalit). Findings spanning from 1950 to the present highlight a significantly eroded beach, attributed to various anthropogenic factors. The results of this thesis illustrate the relevance and constraints of utilizing such instrumentation (video camera and pressure sensor) for investigating back-reef beaches. Seasonal fluctuations in the waterline position, with a retreat of 1.4 m in winter and an advance of 0.9 m in summer, are observed. On average, the reef attenuates 97% of incoming swells and functions as a low-pass filter. Additionally, an analysis of runup on the foreshore reveals that low-frequency waves contribute 50% to this phenomenon, with water level setup contributing up to 40%. This research underscores the critical role of long waves (infragravity (IG) and Very Low Frequency (VLF)) in both submersion and erosion processes. It sets the stage for further exploration in this environmental context, particularly in light of climate change and rising sea levels

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Books on the topic "Remote sensing - video"

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Chezar, Henry. An all-weather time-lapse video recording station. [Menlo Park, Calif.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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E, Thomas Joseph, and Geological Survey (U.S.), eds. An all-weather time-lapse video recording station. [Menlo Park, Calif.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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Chezar, Henry. An all-weather time-lapse video recording station. [Menlo Park, Calif.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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Chezar, Henry. An all-weather time-lapse video recording station. [Menlo Park, Calif.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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Chezar, Henry. An all-weather time-lapse video recording station. [Menlo Park, Calif.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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Geological Survey (U.S.), ed. HVO/RVTS-1: A prototype remote video telemetry system for monitoring the Kilauea east rift zone eruption, 1997. [Menlo Park, CA]: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Thornber, Carl R. HVO/RVTS-1: A prototype remote video telemetry system for monitoring the Kilauea east rift zone eruption, 1997. [Menlo Park, CA]: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Geological Survey (U.S.), ed. HVO/RVTS-1: A prototype remote video telemetry system for monitoring the Kilauea east rift zone eruption, 1997. [Menlo Park, CA]: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Geological Survey (U.S.), ed. HVO/RVTS-1: A prototype remote video telemetry system for monitoring the Kilauea east rift zone eruption, 1997. [Menlo Park, CA]: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Perchanok, M. S. Evaluation of a video system for remote monitoring of winter road surface conditions. Downsview, Ont: Research and Development Branch, Ministry of Transportation, 1994.

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Book chapters on the topic "Remote sensing - video"

1

Silva, Ana Nobre, and Rui Taborda. "Advances in Video Monitoring of the Beach and Nearshore: The Long-Term Perspective." In Remote Sensing and Modeling, 277–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06326-3_11.

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Ruiz, Pablo, Javier Mateos, Gustavo Camps-Valls, Rafael Molina, and Aggelos K. Katsaggelos. "Interactive Pansharpening and Active Classification in Remote Sensing." In Multimodal Interaction in Image and Video Applications, 67–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35932-3_5.

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Jeune, Hayden, Niklas Pechan, Sharn-Konet Reitsma, and Andreas W. Kempa-Liehr. "Spatial Variation Sequences for Remote Sensing Applications with Small Sample Sizes." In Image and Video Technology, 153–66. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0376-0_12.

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Meißner, Henry, Michael Cramer, and Ralf Reulke. "Evaluation of Structures and Methods for Resolution Determination of Remote Sensing Sensors." In Image and Video Technology, 59–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39770-8_5.

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Zhao, Jiao, Jing Han, Chen Feng, and Jian Yao. "A Systematic Scheme for Automatic Airplane Detection from High-Resolution Remote Sensing Images." In Image and Video Technology, 465–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92753-4_36.

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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, 382–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92753-4_30.

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Yu, Zhicheng, Tao Li, and Ke Liu. "Research on Automatic Target Tracking Technology Based on Remote Sensing Video Data." In Lecture Notes in Electrical Engineering, 688–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4163-6_82.

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Brunn, Andreas, Sara Bahloul, Dietrich Hoffmann, and Cody Anderson. "Recent Progress in In-Flight Radiometric Calibration and Validation of the RapidEye Constellation of 5 Multispectral Remote Sensing Satellites." In Image and Video Technology – PSIVT 2015 Workshops, 273–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30285-0_22.

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Álvarez Casado, Constantino, Pauli Räsänen, Le Ngu Nguyen, Arttu Lämsä, Johannes Peltola, and Miguel Bordallo López. "A Distributed Framework for Remote Multimodal Biosignal Acquisition and Analysis." In Communications in Computer and Information Science, 127–46. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_9.

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AbstractIn recent times, several studies have presented single-modality systems for non-contact biosignal monitoring. While these systems often yield estimations correlating with clinical-grade devices, their practicality is limited due to constraints in real-time processing, scalability, and interoperability. Moreover, these studies have seldom explored the combined use of multiple modalities or the integration of various sensors. Addressing these gaps, we introduce a distributed computing architecture designed to remotely acquire biosignals from both radars and cameras. This architecture is supported by conceptual blocks that distribute tasks across sensing, computing, data management, analysis, communication, and visualization. Emphasizing interoperability, our system leverages RESTful APIs, efficient video streaming, and standardized health-data protocols. Our framework facilitates the integration of additional sensors and improves signal analysis efficiency. While the architecture is conceptual, its feasibility has been evaluated through simulations targeting specific challenges in networked remote photoplethysmography (rPPG) systems. Additionally, we implemented a prototype to demonstrate the architectural principles in action, with modules and blocks operating in independent threads. This prototype specifically involves the analysis of biosignals using mmWave radars and RGB cameras, illustrating the potential for the architecture to be adapted into a fully distributed system for real-time biosignal processing.

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Tan, Haibin, Bin Cai, Yuan Xue, Yiling Liu, Yuxin Jin, Renjia Wei, and Hai Yu. "Research on Video Reuse and Structured Spatio-Temporal Big Data Technology Based on 3D Remote Sensing." In Smart Innovation, Systems and Technologies, 133–45. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5184-6_12.

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Conference papers on the topic "Remote sensing - video"

1

Rambabu, Damalla, Rajeshreddy Datla, Vishnu Chalavadi, and C. Krishna Mohan. "A Hybrid Embedding for Generalized Zero-Shot Scene Classification in Remote Sensing Images." In 2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/avss61716.2024.10672594.

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Yang, Shuqin, Haopu Yuan, Tianqi Wang, Rui Zhong, Chenggang Song, Ying Fu, Wenyi Ge, and Xia Yuan. "Procedural Generation of 3D Scenes for Urban Landscape Based on Remote Sensing Images." In 2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/avss61716.2024.10672567.

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Byun, Jungmin, Seokwon Lee, Myeong-Jin Lee, and Woo-Kyung Lee. "Warp-Based Stabilization for Autofocused Video SAR Sequences." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 6438–42. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642472.

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Zuo, Haowen, Hongyang An, Junjie Wu, Kah Chan Teh, Zhongyu Li, and Jianyu Yang. "Video SAR Reconstruction Based on Low-Rank Representation." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 6534–38. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640753.

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Post, Robert P., and Hironori M. Sasaki. "Remote video surveillance systems." In SPIE Defense, Security, and Sensing, edited by Edward M. Carapezza. SPIE, 2009. http://dx.doi.org/10.1117/12.818763.

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Kim, Yong-Sung, Gyu-Hee Park, Seung-Hwan Kim, and Hyung-Joon Cho. "Fast motion detection in coded video streams for a large-scale remote video sensor system." In SPIE Remote Sensing, edited by Bormin Huang, Sebastian López, and Zhensen Wu. SPIE, 2014. http://dx.doi.org/10.1117/12.2067109.

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Saur, Günter, and Wolfgang Krüger. "Short-term change detection for UAV video." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2012. http://dx.doi.org/10.1117/12.975156.

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Maleh, Ray, Frank A. Boyle, Paul B. Deignan, and Jerry W. Yancey. "Interactive video compression for remote sensing." In SPIE Defense, Security, and Sensing, edited by Daniel J. Henry, Beato T. Cheng, Dale C. Linne von Berg, and Darrell L. Young. SPIE, 2011. http://dx.doi.org/10.1117/12.886426.

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Howard, Richard T., Michael L. Book, and Thomas C. Bryan. "Video-based sensor for tracking three-dimensional targets." In Europto Remote Sensing, edited by John D. Gonglewski, Gary W. Kamerman, Anton Kohnle, Ulrich Schreiber, and Christian Werner. SPIE, 2001. http://dx.doi.org/10.1117/12.413832.

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Santos, Lucana, Sebastian López, Gustavo M. Callicó, Jose F. López, and Roberto Sarmiento. "Lossy hyperspectral image compression with state-of-the-art video encoder." In SPIE Remote Sensing, edited by Bormin Huang and Antonio J. Plaza. SPIE, 2011. http://dx.doi.org/10.1117/12.898523.

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Reports on the topic "Remote sensing - video"

1

Mecimore, Ivan, Chuck D. Creusere, and Bion John Merchant. Distributed video coding for arrays of remote sensing nodes : final report. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/992327.

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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Bates, C. Richards, Melanie Chocholek, Clive Fox, John Howe, and Neil Jones. Scottish Inshore Fisheries Integrated Data System (SIFIDS): Work package (3) final report development of a novel, automated mechanism for the collection of scallop stock data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23449.

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[Extract from Executive Summary] This project, aimed at the development of a novel, automated mechanism for the collection of scallop stock data was a sub-part of the Scottish Inshore Fisheries Integrated Data Systems (SIFIDS) project. The project reviewed the state-of-the-art remote sensing (geophysical and camera-based) technologies available from industry and compared these to inexpensive, off-the -shelf equipment. Sea trials were conducted on scallop dredge sites and also hand-dived scallop sites. Data was analysed manually, and tests conducted with automated processing methods. It was concluded that geophysical acoustic technologies cannot presently detect individual scallop but the remote sensing technologies can be used for broad scale habitat mapping of scallop harvest areas. Further, the techniques allow for monitoring these areas in terms of scallop dredging impact. Camera (video and still) imagery is effective for scallop count and provide data that compares favourably with diver-based ground truth information for recording scallop density. Deployment of cameras is possible through inexpensive drop-down camera frames which it is recommended be deployed on a wide area basis for further trials. In addition, implementation of a ‘citizen science’ approach to wide area recording is suggested to increase the stock assessment across the widest possible variety of seafloor types around Scotland. Armed with such data a full, statistical analysis could be completed and data used with automated processing routines for future long-term monitoring of stock.

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