scholarly journals Airborne GNSS Reflectometry for Water Body Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 163
Author(s):  
Hamza Issa ◽  
Georges Stienne ◽  
Serge Reboul ◽  
Mohamad Raad ◽  
Ghaleb Faour
Keyword(s):  
Water Body ◽  
Detection Algorithm ◽  
High Rate ◽  
Signal Segmentation ◽  
Gnss Reflectometry ◽  
On Line ◽  
Gnss Measurements ◽  
Processing Techniques ◽  
Land Water ◽  
Radar Technique

This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz ) and spatial resolution (order of 10 to 100 m2). We also show that we can localize the edges of the detected water body surfaces at meter accuracy.

High-Rate GNSS Reflectometry for Water Body Detection Using Low Altitude Airborne Carrier

2021 ◽  
Author(s):  
Hamza Issa ◽  
Georges Stienne ◽  
Serge Reboul ◽  
Mohamad Raad ◽  
Ghaleb Faour
Keyword(s):  
Water Body ◽  
High Rate ◽  
Gnss Reflectometry ◽  
Low Altitude

scholarly journals Combining High- and Low-Rate Geodetic Data Analysis for Unveiling Rapid Magma Transfer Feeding a Sequence of Violent Summit Paroxysms at Etna in Late 2015

2021 ◽  
Vol 11 (10) ◽  
pp. 4630
Author(s):  
Alessandro Bonforte ◽  
Flavio Cannavò ◽  
Salvatore Gambino ◽  
Francesco Guglielmino
Keyword(s):  
Ground Deformation ◽  
High Rate ◽  
Rate Data ◽  
Scale Analysis ◽  
Feeding System ◽  
Magma Sources ◽  
Mt Etna ◽  
Multi Temporal ◽  
Gnss Measurements ◽  
Low Rate

We propose a multi-temporal-scale analysis of ground deformation data using both high-rate tilt and GNSS measurements and the DInSAR and daily GNSS solutions in order to investigate a sequence of four paroxysmal episodes of the Voragine crater occurring in December 2015 at Mt. Etna (Italy). The analysis aimed at inferring the magma sources feeding a sequence of very violent eruptions, in order to understand the dynamics and to image the shallow feeding system of the volcano that enabled such a rapid magma accumulation and discharge. The high-rate data allowed us to constrain the sources responsible for the fast and violent dynamics of each paroxysm, while the cumulated deformation measured by DInSAR and daily GNSS solutions, over a period of 12 days encompassing the entire eruptive sequence, also showed the deeper part of the source involved in the considered period, where magma was stored. We defined the dynamics and rates of the magma transfer, with a middle-depth storage of gas-rich magma that charges, more or less continuously, a shallower level where magma stops temporarily, accumulating pressure due to the gas exsolution. This machine-gun-like mechanism could represent a general conceptual model for similar events at Etna and at all volcanoes.

scholarly journals CAD System for Lung Cancer and its Stages Detection using Image Processing Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1612-1615
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
False Positive ◽  
False Negative ◽  
Region Growing ◽  
High Rate ◽  
Support Vector ◽  
Image Region ◽  
Image Processing Techniques ◽  
Processing Techniques

The Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system

scholarly journals INTEGRATED SHORELINE EXTRACTION APPROACH WITH USE OF RASAT MS AND SENTINEL-1A SAR IMAGES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 445-449 ◽  
Author(s):  
N. Demir ◽  
S. Oy ◽  
F. Erdem ◽  
D. Z. Şeker ◽  
B. Bayram
Keyword(s):  
Water Body ◽  
Accuracy Assessment ◽  
Extraction Methods ◽  
Training Data ◽  
Sar Images ◽  
Data Set ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Body Segmentation ◽  
Land Water

Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

Beyond high frequency monitoring: an optimised automatic sampling

2021 ◽  
Author(s):  
Jérémy Mougin
Keyword(s):  
Organic Matter ◽  
Water Body ◽  
High Frequency ◽  
Passive Samplers ◽  
Size Exclusion ◽  
Strong Impact ◽  
Small Stream ◽  
Automatic Sampling ◽  
Frequency Monitoring ◽  
On Line

<p>Beyond high frequency monitoring : an optimised automatic sampling</p><p>Mougin Jérémy, Superville Pierre-Jean, Cornard Jean-Paul, Billon Gabriel</p><p> </p><p>In order to improve the representativity of samples when monitoring a water body, efforts have been made these last years to develop new methodologies to replace grab samples. Passive samplers have allowed to have measurement averaged over several days and represented a first step. High frequency monitoring (usually one measure per hour), either in situ or on-line, led to the observations of daily cycles or transitory phenomena that were not suspected beforehand.</p><p>However, such method is usually difficult to implement for some trace analytes (e.g. trace metals or pesticides) or for some specific analysis (e.g. size exclusion chromatography on natural organic matter). Automatic sampling and analysis in the lab can be a solution, but it becomes very labor intensive as soon as the sampling frequency is high. Luck is also needed as a long sampling period can sometimes lead to very few variations if the water system is stable. In order to optimise the automatic sampling, a new methodology has been developped in this project.</p><p>A multiparameter probe measuring general parameters (temperature, pH, turbidity, ORP, conductivity, dissolved oxygen and two fluorometers for organic matter) was coupled with an automatic filtering sampler. The data from the probe are processed on-line and an algorithm decides if the geochemical situation in the water body seems new enough to trigger the sampling, based on previously sampled waters. The aim of this device is to collect the right number of samples with the best representativeness of phenomena taking place in the environment.</p><p>This method will be tested over a year in 2021 in order to monitor the dissolved organic matter in a small stream with both rural and urban contamination. These high-frequency measurements and samplings could make it possible to better define the sources and dynamics of the organic matter that has a strong impact on the quality of watercourses.</p>

scholarly journals A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 786 ◽  
Author(s):  
Yida Zhu ◽  
Haiyong Luo ◽  
Qu Wang ◽  
Fang Zhao ◽  
Bokun Ning ◽  
...  
Keyword(s):  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Detection Accuracy ◽  
Sliding Windows ◽  
Switching Delay ◽  
Spatial Geometry ◽  
Gnss Measurements ◽  
Indoor And Outdoor

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.

scholarly journals Research on Edge Detection Algorithm Based on Line Laser Scanning

Procedia CIRP ◽  
2019 ◽  
Vol 84 ◽  
pp. 1101-1106
Author(s):  
Jingyi Tang ◽  
Xiaoqun Tan ◽  
Xi Li ◽  
Binbin Wei ◽  
Zhanxi Wang ◽  
...  
Keyword(s):  
Edge Detection ◽  
Laser Scanning ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
On Line
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