REAL-TIME PROCESSING OF OIL SPILL REMOTE SENSING DATA1

1987 ◽  
Vol 1987 (1) ◽  
pp. 71-73
Author(s):  
Yvon J. F. Le Guen ◽  
Marc Brussieux ◽  
Rolland Burkhalter
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Oil Spill ◽  
Infrared Emissivity ◽  
Real Time Processing ◽  
Time Processing ◽  
Oil Slick ◽  
Processing Program ◽  
Thermal Infrared Imagery

ABSTRACT The good quality of thermal infrared imagery and the relation between infrared emissivity and the thickness of an oil slick have led to the development of a visualizing rack and an image processing program. This system, tested on board a surveillance aircraft, has permitted on-site estimation within 20 seconds of the total volume of a 28 cubic meter slick with less than 15% error.

scholarly journals Sky Monitoring System for Flying Object Detection Using 4K Resolution Camera

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7071
Author(s):  
Takehiro Kashiyama ◽  
Hideaki Sobue ◽  
Yoshihide Sekimoto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Monitoring System ◽  
State Of The Art ◽  
Weather Conditions ◽  
Network Technology ◽  
Real Time Processing ◽  
Time Processing ◽  
High Rise ◽  
Neural Network Technology

The use of drones and other unmanned aerial vehicles has expanded rapidly in recent years. These devices are expected to enter practical use in various fields, such as taking measurements through aerial photography and transporting small and lightweight objects. Simultaneously, concerns over these devices being misused for terrorism or other criminal activities have increased. In response, several sensor systems have been developed to monitor drone flights. In particular, with the recent progress of deep neural network technology, the monitoring of systems using image processing has been proposed. This study developed a monitoring system for flying objects using a 4K camera and a state-of-the-art convolutional neural network model to achieve real-time processing. We installed a monitoring system in a high-rise building in an urban area during this study and evaluated the precision with which it could detect flying objects at different distances under different weather conditions. The results obtained provide important information for determining the accuracy of monitoring systems with image processing in practice.

Measurement of Temperature Field in Laser Molten Pool by CCD Based on DSP

2008 ◽  
Vol 392-394 ◽  
pp. 693-696
Author(s):  
Z. Cao ◽  
Xi Chen Yang
Keyword(s):  
Image Processing ◽  
Temperature Field ◽  
Real Time ◽  
Measurement System ◽  
Molten Pool ◽  
Software Process ◽  
Real Time Processing ◽  
Time Processing ◽  
Gradient Image ◽  
Time Problem

In this paper, a measurement system of temperature field in laser molten pool by CCD based on DSP is presented. The paper also presents the system of hardware, software process, and the method of image processing by DSP. To solve the real time problem in the processing of measuring the temperature field, the system uses DSP as the main CPU, processing the image without a computer. By this method, the system can become simple and efficient and measure the laser molten pool temperature field quickly and exactly. Gradient image of temperature field is displayed on LCD after real time processing by this system. The final picture of temperature field can be directly analyzed.

scholarly journals Crop Row Detection in Maize Fields Inspired on the Human Visual Perception

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
J. Romeo ◽  
G. Pajares ◽  
M. Montalvo ◽  
J. M. Guerrero ◽  
M. Guijarro ◽  
...  
Keyword(s):  
Image Processing ◽  
Vision System ◽  
Normal Operation ◽  
Line Detection ◽  
Perspective Projection ◽  
Human Visual Perception ◽  
Real Time Processing ◽  
Time Processing ◽  
Maximum Accumulation ◽  
Operation Process

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.

scholarly journals OIL SLICK FATE IN A REGION OF STRONG TIDAL CURRENTS

1974 ◽  
Vol 1 (14) ◽  
pp. 130
Author(s):  
G. Drapeau ◽  
W. Harrison ◽  
W. Bien ◽  
P. Leinonen
Keyword(s):  
Remote Sensing ◽  
Mathematical Models ◽  
Oil Spill ◽  
Field Measurements ◽  
Tidal Currents ◽  
Lawrence Estuary ◽  
Oil Slick ◽  
Scale Models ◽  
St Lawrence Estuary ◽  
Made In

This study examines the drifting, spreading and aging of small slicks of crude oil in the middle St. Lawrence Estuary. This region was chosen because it is well documented with field measurements, hydraulic scale models, and mathematical models; and also because it is becoming a strategic area for the development of supertanker ports for 300,000 and possibly 500,000 ton tankers. Two controlled releases of Venezuelan crude (370 and 800 litres) were made in November 1972, as ice began to form in the St. Lawrence Estuary. The experiments were supported by the Canada Centre for Remote Sensing which carried out extensive airborne monitoring. The results indicate that it is impossible either to recover or to disperse small spills of oil in this region of strong tidal currents. Models also predict slick motion poorly. The alternative is to construct slick-drift roses that will indicate areas of expected beaching and assist in deployment of oil-spill clean-up technology.

scholarly journals A library for fMRI real-time processing systems in python (RTPSpy) with comprehensive online noise reduction, fast and accurate anatomical image processing, and online processing simulation

2021 ◽  
Author(s):  
Masaya Misaki ◽  
Jerzy Bodurka ◽  
Martin P Paulus
Keyword(s):  
Image Processing ◽  
Data Processing ◽  
Real Time ◽  
Easy Access ◽  
External Application ◽  
Real Time Processing ◽  
Time Processing ◽  
Target Signal ◽  
Processing Pipeline ◽  
Anatomical Image

We introduce a python library for real-time fMRI (rtfMRI) data processing systems, Real-Time Processing System in python (RTPSpy), to provide building blocks for a custom rtfMRI application with extensive and advanced functionalities. RTPSpy is a library package including 1) a fast, comprehensive, and flexible online fMRI denoising pipeline comparable to offline processing, 2) utilities for fast and accurate anatomical image processing to define a target region on-site, 3) a simulation system of online fMRI processing to optimize a pipeline and target signal calculation, 4) interface to an external application for feedback presentation, and 5) a boilerplate graphical user interface (GUI) integrating operations with RTPSpy library. Since online fMRI data processing cannot be equivalent to offline, we discussed the limitations of online analysis and their solutions in the RTPSpy implementation. We developed a fast and accurate anatomical image processing script with fast tissue segmentation (FastSeg), image alignment, and spatial normalization, utilizing the FastSurfer, AFNI, and ANTs. We confirmed that the FastSeg output was comparable with FreeSurfer, and could complete all the anatomical image processing in a few minutes. Thanks to its highly modular architecture, RTPSpy can easily be used for a simulation analysis to optimize a processing pipeline and target signal calculation. We present a sample script for building a real-time processing pipeline and running a simulation using RTPSpy. The library also offers a simple signal exchange mechanism with an external application. An external application can receive a real-time neurofeedback signal from RTPSpy in a background thread with a few lines of script. While the main components of the RTPSpy are the library modules, we also provide a GUI class for easy access to the RTPSpy functions. The boilerplate GUI application provided with the package allows users to develop a customized rtfMRI application with minimum scripting labor. Finally, we discussed the limitations of the package regarding environment-specific implementations. We believe that RTPSpy is an attractive option for developing rtfMRI applications highly optimized for individual purposes. The package is available from GitHub (https://github.com/mamisaki/RTPSpy) with GPL3 license.

Computational Benefits, Limitations and Techniques of Parallel Image Processing

2020 ◽  
pp. 1-9
Author(s):  
Maha Heng Li ◽  
Marinka Yu Zhang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Comparative Evaluation ◽  
Parallel Imaging ◽  
Feature Classification ◽  
Parallel Image Processing ◽  
Parallel Image

The main purpose of digitalized image processing is to enhance the quality of images and subsequently to facilitate the process of feature classification and extraction. This process is effectively utilized in healthcare imaging, computer visioning, astronomy, meteorology, and remote sensing among other essential fields. The main issue is that this technique takes a lot of time even though it provides convenient and efficient means of addressing the prevailing issue. In this paper, we shall provide a comparative evaluation of the present contributions adding to the application of parallel image processing including their limitations and benefits. Another key segment of research in this paper is to evaluate parallel computing, presently available techniques, tools and architecture in different image processing applications. As such, this paper is purposed to critically evaluate the role of parallel imaging in the field of healthcare imaging.

Use of Geo-Informatics on Nearshore Management in Oil Spill Accidents

2001 ◽  
Vol 2001 (1) ◽  
pp. 489-495
Author(s):  
Shintaro Goto ◽  
Sang-Woo Kim
Keyword(s):  
Remote Sensing ◽  
Information Management ◽  
Oil Spill ◽  
Warning System ◽  
Remote Sensing Data ◽  
Information Provision ◽  
Interim Result ◽  
Oil Slick ◽  
Recovery Support

ABSTRACT From the lessons after the Nakhodka oil spill in January 1997, oil slick detection by using remote-sensing data and assimilating the data to the simulation program is important for monitoring the oil drift pattern. For this object, the authors are going to construct the oil spill warning system for estimating the oil drift pattern using a remote-sensing/numerical simulation model. Additionally, they plan to use this system for restoring oil spill damage domestically, such as estimating the ecological damage and making the priority for restoring the oil spilled shoreline. This report is intended to summarize the role of geo-informatics in the oil spill accident by not only paying attention to the effect of information provision/information management via the map, but also reporting the interim result in part based on the details discussed in the processes of recovery support and environmental impact assessment during the Nakhodka accident.

scholarly journals Near-real-time processing of a ceilometer network assisted with sun-photometer data: monitoring a dust outbreak over the Iberian Peninsula

2017 ◽  
Vol 17 (19) ◽  
pp. 11861-11876 ◽  
Author(s):  
Alberto Cazorla ◽  
Juan Andrés Casquero-Vera ◽  
Roberto Román ◽  
Juan Luis Guerrero-Rascado ◽  
Carlos Toledano ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Signal To Noise Ratio ◽  
Backscatter Coefficient ◽  
Vast Number ◽  
Real Time Processing ◽  
Time Processing ◽  
Dense Networks ◽  
Lidar Measurements ◽  
Aerosol Characterization

Abstract. The interest in the use of ceilometers for optical aerosol characterization has increased in the last few years. They operate continuously almost unattended and are also much less expensive than lidars; hence, they can be distributed in dense networks over large areas. However, due to the low signal-to-noise ratio it is not always possible to obtain particle backscatter coefficient profiles, and the vast number of data generated require an automated and unsupervised method that ensures the quality of the profiles inversions. In this work we describe a method that uses aerosol optical depth (AOD) measurements from the AERONET network that it is applied for the calibration and automated quality assurance of inversion of ceilometer profiles. The method is compared with independent inversions obtained by co-located multiwavelength lidar measurements. A difference smaller than 15 % in backscatter is found between both instruments. This method is continuously and automatically applied to the Iberian Ceilometer Network (ICENET) and a case example during an unusually intense dust outbreak affecting the Iberian Peninsula between 20 and 24 February 2016 is shown. Results reveal that it is possible to obtain quantitative optical aerosol properties (particle backscatter coefficient) and discriminate the quality of these retrievals with ceilometers over large areas. This information has a great potential for alert systems and model assimilation and evaluation.

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