Relevant bibliographies by topics / Rain detection

Contents

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

Academic literature on the topic 'Rain detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Rain detection"

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Fu, Fangfa, Yao Wang, Fengchang Lai, Weizhe Xu, and Jinxiang Wang. "Efficient rain–fog model for rain detection and removal." Journal of Electronic Imaging 29, no. 02 (April 7, 2020): 1. http://dx.doi.org/10.1117/1.jei.29.2.023020.

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Akanni, J., A. O. Ojo, A. Abdulwahab, A. A. Isa, and O. Ogunbiyi. "Development and Implementation of a Prototype Automatic Rain-Sensor Car Wiper System." Journal of Applied Sciences and Environmental Management 26, no. 11 (November 30, 2022): 1821–26. http://dx.doi.org/10.4314/jasem.v26i11.13.

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Various studies have been conducted over the years on how to reduce driver distractions while driving, but with little effort on the distraction that could be caused by manually operated wipers while driving whenever it rains. Drivers frequently take their hands off the steering to turn ON/OFF and adjust the wiper speed when driving during rain, which causes a loss of concentration and increases the risk of a car accident. This paper presents an automatic car wiper prototype system that adjusts the speed of the wiper based on the intensity of the rain. The system also includes an audio alert that warns the driver to stop driving during heavy rain. The rain sensor/intensity and servo motor; which regulates the wiper's speed, were interfaced by an ATMega328 (Arduino Uno A000066). It performed satisfactorily, with average response times of 0.78 seconds, 1.95 seconds, and 6 seconds for rain water detection, increasing rain intensity, and no rain detection respectively. The wiper speed was 15 rpm at moderate rain intensity and 32 rpm at heavy rain intensity. The wiper average response time and speed shows that it is a system that eliminate delay as compare to manually operated car wiper system. The developed system will reduce driver distractions while driving thereby reduces the risk of a car accident. As a result, this system can be combined with new technologies seen in contemporary vehicles.
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Upadhyaya, S., and R. A. A. J. Ramsankaran. "Support Vector Machine (SVM) based Rain Area Detection from Kalpana-1 Satellite Data." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 21–27. http://dx.doi.org/10.5194/isprsannals-ii-8-21-2014.

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Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple <i>TIR</i> threshold used in Global Precipitation Index (GPI) technique and <i>Roca</i> method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probabil ity of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new SVM based index performs much better than the earlier indices.
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Hassim, Raima, Hamzah Asyrani Sulaiman, and Abdullah Bade. "An Efficient Rain Streaks Detection and Removal for Single Image Using Hybridization of Rain Detection and Rain Removal Technique (HyDRA)." Advanced Science Letters 24, no. 2 (February 1, 2018): 1027–31. http://dx.doi.org/10.1166/asl.2018.10680.

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Dong, Jianzhi, Wade T. Crow, and Rolf Reichle. "Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources." Journal of Hydrometeorology 21, no. 10 (October 1, 2020): 2419–29. http://dx.doi.org/10.1175/jhm-d-20-0097.1.

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AbstractRain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products.
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Kingsley, Kumah K., Ben H. P. Maathuis, Joost C. B. Hoedjes, Donald T. Rwasoka, Bas V. Retsios, and Bob Z. Su. "Rain Area Detection in South-Western Kenya by Using Multispectral Satellite Data from Meteosat Second Generation." Sensors 21, no. 10 (May 19, 2021): 3547. http://dx.doi.org/10.3390/s21103547.

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This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG’s reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.g., high optical thickness and effective radius, have high rain probabilities and intensities. Next, a gradient based adaptive correction technique that relies on rain area-specific parameters was developed to reduce the number and sizes of the detected rain areas. The daytime detection with optical (VIS0.6) and near IR (NIR1.6) reflectance data achieved the best detection skill. For nighttime, detection with thermal IR brightness temperature differences of IR3.9-IR10.8, IR3.9-WV73 and IR108-WV62 showed the best detection skill based on general categorical statistics. Compared to the Global Precipitation Measurement (GPM) Integrated Mult-isatellitE Retrievals for GPM (IMERG) and the gauge station data from the southwest of Kenya, the model showed good agreement in the spatial dynamics of the detected rain area and rain rate.
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Bauer, Peter, Dirk Burose, and Jörg Schulz. "Rain detection over land surfaces using passive microwave satellite data." Meteorologische Zeitschrift 11, no. 1 (March 5, 2002): 37–48. http://dx.doi.org/10.1127/0941-2948/2002/0011-0037.

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Zhao, Yuan, Nicolas Longépé, Alexis Mouche, and Romain Husson. "Automated Rain Detection by Dual-Polarization Sentinel-1 Data." Remote Sensing 13, no. 16 (August 10, 2021): 3155. http://dx.doi.org/10.3390/rs13163155.

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Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach.
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Son, Chang-Hwan, and Xiao-Ping Zhang. "Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary." Journal of Imaging Science and Technology 64, no. 3 (May 1, 2020): 30501–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.3.030501.

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Abstract Rain removal is essential for achieving autonomous driving because it preserves the details of objects that are useful for feature extraction and removes the rain structures that hinder feature extraction. Based on a linear superposition model in which the observed rain image is decomposed into two layers, a rain layer and a non-rain layer, conventional rain removal methods estimate these two layers alternatively from an observed single image based on prior modeling. However, the prior knowledge used for the rain structures is not always correct because various types of rain structures can be observed in the rain images, which results in inaccurate rain removal. Therefore, in this article, a novel rain removal method based on the use of a scribbled rain image set and a new shrinkage-based sparse coding model is proposed. The scribbled rain images have information about which pixels have rain structures. Thus, various types of rain structures can be modeled, owing to the abundance of rain structures in the rain image set. To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, the proposed shrinkage-based sparse coding model determines how much to reduce the sparse codes of the rain dictionary and maintain the sparse codes of the non-rain dictionary for accurate rain removal. Experimental results verified that the proposed shrinkage-based sparse coding model could remove rain structures and preserve objects’ details due to the REC- or DCNN-based rain detection using the scribbled rain image set. Moreover, it was confirmed that the proposed method is more effective at removing rain structures from similar objects’ structures than conventional methods.
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Upadhyaya, Shruti, and R. Ramsankaran. "Multi-Index Rain Detection: A New Approach for Regional Rain Area Detection from Remotely Sensed Data." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2314–30. http://dx.doi.org/10.1175/jhm-d-14-0006.1.

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Abstract In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIRt &lt; 260 K and TIRt − WVt &lt; 19 K, where TIRt and WVt are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 × 5 pixels at time t (mean5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.
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Dissertations / Theses on the topic "Rain detection"

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Webster, Dereck D. "Automatic Rain Drop Detection for Improved Sensing in Automotive Computer Vision Applications." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9154.

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The presence of raindrop induced distortion can have a significant negative impact on computer vision applications. Here we address the problem of visual raindrop distortion in standard colour video imagery for use in non-static, automotive computer vision applications where the scene can be observed to be changing over subsequent consecutive frames. We utilise current state of the art research conducted into the investigation of salience mapping as means of initial detection of potential raindrop candidates. We further expand on this prior state of the art work to construct a combined feature rich descriptor of shape information (Hu moments), isolation of raindrops pixel information from context, and texture (saliency derived) within an improved visual bag of words verification framework. Support Vector Machine and Random Forest classification were utilised for verification of potential candidates, and the effects of increasing discrete cluster centre counts on detection rates were studied. This novel approach of utilising extended shape information, isolation of context, and texture, along with increasing cluster counts, achieves a notable 13% increase in precision (92%) and 10% increase in recall (86%) against prior state of the art. False positive rates were also observed to decrease with a minimal false positive rate of 14% observed.
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Köylüoglu, Tugay, and Lukas Hennicks. "Evaluating rain removal image processing solutions for fast and accurate object detection." Thesis, KTH, Mekatronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254446.

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Autonomous vehicles are an important topic in modern day research, both for the private and public sector. One of the reasons why self-driving cars have not yet reached consumer market is because of levels of uncertainty. This is often tackled with multiple sensors of different kinds which helps gaining robust- ness in the vehicle’s system. Radars, lidars and cameras are often the sensors used and the expenses can rise up quickly, which is not always feasible for different markets. This could be addressed with using fewer, but more robust sensors for visualization. This thesis addresses the issue of one particular failure mode for camera sensors, which is reduced view range affected by rainy weather. Kalman filter and discrete wavelet transform with bilateral filtering are evaluated as rain removal algorithms and tested with the state-of-the-art object detection algorithm, You Only Look Once (YOLOv3). Filtered videos in daylight and evening light were tested with YOLOv3 and results show that the accuracy is not improved enough to be worth implementing in autonomous vehicles. With the graphics card available for this thesis YOLOv3 is not fast enough for a vehicle to stop in time when driving in 110km/h and an obstacle appears 80m ahead, however an Nvidia Titan X is assumed to be fast enough. There is potential within the research area and this thesis suggests that other object detection methods are evaluated as future work.
Autonoma fordon är för privat samt offentlig sektor ett viktigt område i modern forskning. Osäkerheten med autonoma fordon är en viktig anledning till varför de idag inte nått konsumentmarknaden. Systemen för autonoma fordon blir mer robusta med inkludering av flera sensorer av olika typer, vilka oftast är kameror, radar och lidars. Fordon med dessa sensorer kan snabbt öka i pris vilket gör dem mindre tillgängliga för olika marknader. Detta skulle kunna lösas med färre sensorer som däremot är mer robusta. Denna avhandling diskuterar problemet med en specific felmodell för kameror, vilket är minskat synfält som påverkas av regnigt väder. Kalman filter och diskret vågkomponent-transformation med bilateral filtrering utvärderades som regnborttagningsalgoritmer och testades med You Only Look Once (YOLOv3), en modern objektigenkänningsmetod. Filtrerade videofilmer i dagstid och kvällstid testades med YOLOv3 och resultaten visade att noggrannheten inte ökade tillräckligt mycket för att vara användbara för autonoma fordon. Med grafikkorten tillgängliga för denna avhandling är inte YOLOv3 snabb nog för ett fordon att hinna stanna i tid före kollision om bilen kör i 110km/h och ett föremål dyker upp 80m framför. Däremot antas det att fordon utrustade med Nvidias Titan X borde hinna stanna i tid före kollision. Avhandlingen ser däremot potential inom detta forskningsområde och föreslår att liknande test fast med andra objektigenkänningsmetoder bör utföras.
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Ferroudj, Meriem. "Detection of rain in acoustic recordings of the environment using machine learning techniques." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/82848/1/Meriem_Ferroudj_Thesis.pdf.

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This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.
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Ma, Bin-Bing. "Passive acoustic detection and measurement of rainfall at sea and an empirical ocean ambient sound model /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/11045.

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Allen, Jeffrey R. "An Analysis of SeaWinds Simultaneous Wind/Rain Retrieval in Severe Weather Events." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd704.pdf.

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Marinaro, Ralph Michael. "Investigation of water vapor effects on the detection of nitric acid vapor with the tungstic acid technique." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/71262.

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An automated tungstic acid technique (TAT) has been successfully used to measure gaseous HNO₃ in the presence of water vapor. The TAT is based on the diffusion of gaseous HNO₃ to the interior walls of a tube coated with tungsten VI oxide (WO₃), where it is selectively chemisorbed. The collected HNO₃ sample is thermally desorbed from the WO₃ surface, as NO, and measured by a chemiluminescent oxides of nitrogen analyzer. The integrated analyzer response is directly proportional to the nitric acid collected. Based on nitric acid hydration characteristics, a decrease in the diffusion coefficient and thus collection efficiency for denuder type measurement techniques may result with increased atmospheric water vapor (i.e., relative humidity). This study emphasizes the effect of water vapor (i.e., relative humidity) as a potential interferent for HNO₃ collection with the TAT system. The effect of water vapor (< 78% RH) on the collection efficiency for HNO₃ with the tungstic acid technique is negligible at 25°C, but is significant only at elevated sampling temperatures. This threshold effect is further substantiated and eliminated when a modified sampling collection system was designed with coolant capabilities. The new design has been tested to sub-part-per-billion (NOx analyzer detection limit) levels with minimal loss of gaseous HNO₃ signal, thereby increasing sensitivity to atmospheric HNO₃ concentrations and maintaining the gas/aerosol sample integrity.
Ph. D.
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Liu, Zhongxun. "Modélisation des signatures radar des tourbillons de sillage par temps de pluie." Thesis, Toulouse, ISAE, 2013. http://www.theses.fr/2013ESAE0015/document.

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La surveillance des turbulences de sillage par radar en temps de pluie présente un intérêt à la fois pratique et scientifique. Cette thématique a été traitée à travers trois étapes successives.Tout d’abord, le mouvement et la distribution des gouttes d’eau dans les vortex ont été modélisés et simulés. A partir de l’équation de la dynamique appliquée sur une goutte d’eau, une méthode de calcul de la trajectoire des gouttes d’eau et de leur concentration dans les turbulences de sillage a été proposée. Ensuite, deux simulateurs de réponse radar des gouttes d’eau dans et autour des vortex ont été proposés. Ces deux simulateurs ont été utilisés pour reproduire des configurations expérimentales, et une comparaison préliminaire avec les mesures a montré une concordance intéressante entre mesures et simulations en bande X et W. Enfin, l’interprétation de la signature radar des gouttes de pluie dans les vortex a été présentée. La dépendance de la signature envers différents paramètres, à savoir l’intensité de précipitation, la circulation des vortex et les paramètres radar, a été étudiée pour des turbulences de sillage générées par différents types d’avions. Une méthode de détection des turbulences de sillage basée sur la largeur du spectre Doppler des gouttes de pluie et un algorithme permettant d’estimer les caractéristiques des turbulences de sillage ont été proposées. La signature radar des turbulences de sillage par temps de pluie a été modélisée et analysée dans cette thèse. Les résultats de simulations ont démontré les capacités du radar pour la détection de ces turbulences. Les méthodes développées dans cette thèse pourront être utilisées pour le dimensionnement de systèmes radar dédiés à la surveillance des turbulences de sillage par temps de pluie
Nvestigation on radar monitoring of wake vortices in rainy weather is of both scientific and practical interests. This topic hasbeen tackled through three successive steps during this thesis.Firstly, the motion of raindrops in wake vortices has been modeled and simulated. The equation of the motion has been derived and the methodology to compute the raindrops' trajectory and distribution in the flow induced by the wakevortices has been proposed. Secondly, two simulators have been developed for evaluating the radar signatures of raindropsin wake vortices. Those simulators have been used to reproduce experimental configurations and the comparison betweenmeasured and simulated signature has shown an interesting agreement at X and W band. Lastly, the interpretation of radarsignatures of raindrops in wake vortices has been presented. The dependence of radar signatures on rain rate, vortexcirculation and radar parameters has been studied. A wake vortex detection method based on the analysis of Dopplerspectrum width of raindrops and a methodology to estimate the wake vortex characteristics have been proposed.The radar signatures of wake vortices in rainy weather have been modeled and analyzed in this thesis. The simulationresults have demonstrated the capability of radar to detect wake vortex in rainy weather. The methodologies developed inthis thesis can be further exploited for designing new wake vortex radar systems
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Sengupta, Nina. "Detection and prediction of biodiversity patterns as a rapid assessment tool in the tropical forest of East Usambara, Eastern Arc Mountains, Tanzania." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/30272.

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As a strategy to conserve tropical rainforests of the East Usambara block of the Eastern Arc Mountains, Tanzania, I developed a set of models that can identify above-average tree species richness areas within the humid forests. I developed the model based on geo-referenced field data and satellite image-based variables from the Amani Nature Reserve, the largest forest sector in the East Usambara. I then verified the model by applying it to the Nilo Forest Reserve. The field data, part of the Tanzanian National Biodiversity Database, were collected by Frontier-Tanzania between 1999 and 2001, through the East Usambara Conservation Area Management Program, Government of Tanzania. The field data used are rapidly collectible by people with varied backgrounds and education. I gathered spectral reflectance values from pixels in the Landsat Enhanced Thematic Mapper (Landsat ETM) image covering the study area that corresponded to the ground sample points. The spectral information from different bands formed the satellite image-based variables in the dataset. The best satellite image logistic regression and discriminant analysis models were based on a single band, raw Landsat ETM mid-infrared band 7 (RB7). In the Amani forest, the RB7-based model resulted in 65.3% overall accuracy in identifying above average tree species locations. When the logistic and discriminant models were applied to Nilo forest sector, the overall accuracy was 62.3%. Of the rapidly collectible field variables, only tree density (number of trees) was selected in the logistic regression and the discriminant analysis models. Logistic and discriminant models using both RB7 and number of trees recorded 76.3% overall accuracy in Amani, and when applied to Nilo, 76.8% accuracy. It is possible to apply and adapt the current set of models to identify above-average tree species richness areas in East Usambara and other forest blocks of the Eastern Arc Mountains. Potentially, managers and researchers can periodically use the model to rapidly assess, monitor, update, and map the tree species rich areas within the forest. The same or similar models could be applied to check their applicability in other humid tropical forest areas.
Ph. D.
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Friberg, Carol Diane. "Preliminary processing and evaluation of radar measurements in satellite-path propagation research." Thesis, Virginia Tech, 1985. http://hdl.handle.net/10919/45722.

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Rain and other precipitation cause attenuation and depolarization of high frequency satellite signals. Some characteristics of rain can be measured by dual-polarized radar. These characteristics can then be used to predict the effects of the rain on satellite-path propagation. This thesis describes briefly the theory of radar and satellite link measurements. Methods for calibrating the equipment and deriving actual experimental values from measured power are presented in detail. A set of computer programs to approximately predict radar and link values from measured rain rate are developed. Predicted and measured values may then be compared by a researcher to evaluate system operation and assess the importance of the event data. A discussion of the use of sampled data and these comparisons concludes the report.
Master of Science
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Tang, Shengjie. "Rail Platform Obstacle Detection Using LabVIEW Simulation." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-19063.

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As the rapid development of the rail transportation industry, rail transportation becomes more popular as a component of urban public transport systems, but the fallen obstacle(s) from the rail platform becomes the terrible hidden danger for the rail transportation. As an enclosed public transport systems, rail transportation creates gathered crowd both on board and on the platform. Although railway is the safest form of land transportation, it is capable of producing lots of casualties, when there is an accident.There are several conventional systems of obstacles detection in platform monitoring systems like stereo visions, thermal scanning, and vision metric scanning, etc. As the traditional detection systems could not achieve the demand of detecting the obstacles on the rail within the platform. In this thesis, the author designs a system within the platform based on laser sensors, virtual instruments technology, and image processing technology (machine vision) to increase the efficiency of detection system. The system is useful for guarantying the safety of rail vehicle when coming into the platform and avoid obstacle(s) on the rail fallen from the platform, having a positive impact on traffic safety to protect lives of people.The author used LabVIEW software to create a simulation environment where the input blocks represent the functionalities of the system, in which simulated train detection and fallen object detection. In this thesis, the author mainly focuses on fallen object detection. For fallen object detection, the author used 2D image processing method to detect obstacle(s), so the function is, before the rail vehicle comes into the platform, the system could detect whether there is fallen obstacle(s) on the rail within the platform, simultaneously categorize size of the obstacle(s), and then alarm for delivering the results.
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Books on the topic "Rain detection"

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Dr, Nazrul Islam Md, and SAARC Meteorological Research Centre, eds. Understanding the rainfall climatology and detection of extreme weather events in SAARC region. Dhaka: SAARC Meteorological Research Centre, 2008.

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Thompson, John Cargill. A matter of conviction ; When the rain stops ; The art of detection ; Parting shot: Four plays. Edinburgh: Diehard, 1995.

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London rain. Rearsby, Leicester: WF Howes Ltd, 2015.

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Peter, Abrahams. Hard rain. New York: Dutton, 1988.

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Peter, Abrahams. Hard rain. (Sevenoaks): New English Library, 1988.

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Weeks, Jules B., and Josephine Napolatano. Passenger rail security: Explosives detection and safety efforts. Hauppauge, N.Y: Nova Science Publishers, 2012.

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Burke, James L. The neon rain. New York: Pocket Books, 1988.

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Cold steel rain. London: Orion, 2001.

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Escaping the rain. London: South Star, 2000.

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Cold steel rain. London: Orion, 2000.

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Book chapters on the topic "Rain detection"

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Mukhopadhyay, Sudipta, and Abhishek Kumar Tripathi. "Important Rain Detection Algorithms." In Combating Bad Weather Part I: Rain Removal from Video, 13–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-02251-7_4.

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Tsunoda, Kin-Ichi, Tomonari Umemura, Kazumasa Ohshima, Sho-Ichi Aizawa, Etsuro Yoshimura, and Ken-Ichi Satake. "Determination and Speciation of Aluminum in Environmental Samples by Cation Exchange High-Performance Liquid Chromatography with High Resolution ICP-MS Detection." In Acid rain 2000, 1589–94. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-007-0810-5_112.

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Mukhopadhyay, Sudipta, and Abhishek Kumar Tripathi. "Impact of Camera Motion on Detection of Rain." In Combating Bad Weather Part I: Rain Removal from Video, 49–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-02251-7_6.

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Mukhopadhyay, Sudipta, and Abhishek Kumar Tripathi. "Probabilistic Approach for Detection and Removal of Rain." In Combating Bad Weather Part I: Rain Removal from Video, 21–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-02251-7_5.

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Ferroudj, Meriem, Anthony Truskinger, Michael Towsey, Liang Zhang, Jinglan Zhang, and Paul Roe. "Detection of Rain in Acoustic Recordings of the Environment." In Lecture Notes in Computer Science, 104–16. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13560-1_9.

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Wang, Yao, Fangfa Fu, Jinjin Shi, Weizhe Xu, and Jinxiang Wang. "Efficient Moving Objects Detection by Lidar for Rain Removal." In Intelligent Computing Methodologies, 697–706. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42297-8_64.

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Ma, Chao-qi, and Zheng-fa Liang. "A Rain Detection and Removal Algorithm Based on Rainy Intensity for Videos in Heavy Rainy Scene." In Advances in Intelligent Systems and Computing, 657–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30874-6_61.

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Mukhopadhyay, Sudipta, and Abhishek Kumar Tripathi. "Meteorological Approach for Detection and Removal of Rain from Videos." In Combating Bad Weather Part I: Rain Removal from Video, 57–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-02251-7_7.

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Guo, Hansong, He Huang, Jianxin Wang, Shaojie Tang, Zhenhua Zhao, Zehao Sun, Yu-E. Sun, Liusheng Huang, and Hengchang Liu. "Tefnut: An Accurate Smartphone Based Rain Detection System in Vehicles." In Wireless Algorithms, Systems, and Applications, 13–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42836-9_2.

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Jiang, Jyun-Yu, Yi-Shiang Tzeng, Pei-Ying Huang, and Pu-Jen Cheng. "Analyzing the Spatiotemporal Effects on Detection of Rain Event Duration." In Information Retrieval Technology, 506–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_46.

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Conference papers on the topic "Rain detection"

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Habi, Hai Victor, and Hagit Messer. "RNN Models for Rain Detection." In 2019 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2019. http://dx.doi.org/10.1109/sips47522.2019.9020603.

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Jha, Rajib Kumar, Sraban Kumar Mohanty, and Anand Maitrey. "Entropy-based rain detection and removal." In 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE). IEEE, 2013. http://dx.doi.org/10.1109/care.2013.6733696.

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Huang, Fei-long, and Yuan-hong Li. "Scanning rain gauge based on photo electricity." In International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, edited by Liwei Zhou. SPIE, 2007. http://dx.doi.org/10.1117/12.790887.

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Bosisio, Ada Vittoria, and Maria P. Cadeddu. "Rain detection from ground-based radiometric measurements: Validation against rain sensor observations." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326273.

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Zhou, Ming, Zhichao Zhu, Rong Deng, and Shuai Fang. "Rain detection and removal of sequential images." In 2011 23rd Chinese Control and Decision Conference (CCDC). IEEE, 2011. http://dx.doi.org/10.1109/ccdc.2011.5968255.

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Nagel, Dieter. "Detection of rain areas with airborne radar." In 2017 18th International Radar Symposium (IRS). IEEE, 2017. http://dx.doi.org/10.23919/irs.2017.8008094.

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Rossi, Lucas, André Backes, and Jefferson Souza. "Rain Gutter Detection in Aerial Images for Aedes aegypti Mosquito Prevention." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13474.

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The detection of Aedes aegypti mosquito is essential in the prevention process of serious diseases such as dengue, yellow fever, chikungunya, and Zika virus. Common approaches consist of surveillance agents who need to enter residences to find and eliminate these outbreaks, but often they are unable to do this work due to the absence or resistance of the resident. This paper proposes an automatic system that uses aerial images obtained through a camera coupled from an Unmanned Aerial Vehicle (UAV) to identify rain gutters from a shed that may be mosquitoes’ foci. We use Digital Image Processing (DIP) techniques to differentiate the objects that may or may not be those foci of the mosquito-breeding. The experimental results show that the system is capable of automatically detecting the appropriately mosquito-breeding location.
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Xue, Xinwei, Xin Jin, Chenyuan Zhang, and Satoshi Goto. "Motion robust rain detection and removal from videos." In 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2012. http://dx.doi.org/10.1109/mmsp.2012.6343435.

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Portabella, Marcos, and Ad Stoffelen. "Towards a QuikSCAT quality control indicator: rain detection." In Europto Remote Sensing, edited by Charles R. Bostater, Jr. and Rosalia Santoleri. SPIE, 2000. http://dx.doi.org/10.1117/12.411701.

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Chen, Xinwei, and Weimin Huang. "Rain Detection From X-Band Marine Radar Images." In 2019 IEEE Radar Conference (RadarConf19). IEEE, 2019. http://dx.doi.org/10.1109/radar.2019.8835559.

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Reports on the topic "Rain detection"

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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.
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