scholarly journals A New Shooting Bouncing Ray Method for Composite Scattering from a Target above the Electrically Large Scope Sea Surface

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Peng Peng ◽  
Guo Lixin
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Sea Surface ◽  
Radar Target ◽  
Hybrid Scheme ◽  
Ray Method ◽  
Electromagnetic Interactions ◽  
X Band ◽  
Large Scope ◽  
Low Altitude

A new shooting and bouncing ray (SBR) simulator based on the hybrid scheme of GO/PO/SDFM/EEC method is developed for the accurate prediction of composite scattering from a low altitude target above the electrically very-large-scale sea surface. It can adequately deal with the complex local electromagnetic interactions between the target and the large scope sea surface. The method is compared with the exact computational electromagnetic solver FEKO-MLFMM to validate its accuracy and efficiency. Then, it is applied to simulate the bistatic and monostatic scattering characteristics of an airplane above the electrically large sea surface at X-band, for different sea states. The results reveal the contributions from the target, sea surface, and interactions, which are of significance for radar target detection and remote sensing in real maritime environments.

scholarly journals Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

2020 ◽  
Vol 12 (12) ◽  
pp. 2013
Author(s):  
Konstantinos Topouzelis ◽  
Dimitris Papageorgiou ◽  
Alexandros Karagaitanakis ◽  
Apostolos Papakonstantinou ◽  
Manuel Arias Ballesteros
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Experiments ◽  
Spectral Unmixing ◽  
Sea Surface ◽  
Spectral Signature ◽  
Unmanned Aerial Systems ◽  
Real Environment ◽  
Promising Tool ◽  
Sentinel 2

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

scholarly journals A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

2019 ◽  
Vol 11 (19) ◽  
pp. 2257
Author(s):  
Ji-Yeon Baek ◽  
Young-Heon Jo ◽  
Wonkook Kim ◽  
Jong-Seok Lee ◽  
Dawoon Jung ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Sea Water ◽  
Two Dimensions ◽  
Sea Surface ◽  
Measurement Unit ◽  
Observation System ◽  
Low Altitude ◽  
Multispectral Camera

In this study, a low-altitude remote sensing (LARS) observation system was employed to observe a rapidly changing coastal environment-owed to the regular opening of the sluice gate of the Saemangeum seawall-off the west coast of South Korea. The LARS system uses an unmanned aerial vehicle (UAV), a multispectral camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU) module to acquire geometry information. The UAV system can observe the coastal sea surface in two dimensions with high temporal (1 s−1) and spatial (20 cm) resolutions, which can compensate for the coarse spatial resolution of in-situ measurements and the low temporal resolution of satellite observations. Sky radiance, sea surface radiance, and irradiance were obtained using a multispectral camera attached to the LARS system, and the remote sensing reflectance (Rrs) was accordingly calculated. In addition, the hyperspectral radiometer and in-situ chlorophyll-a concentration (CHL) measurements were obtained from a research vessel to validate the Rrs observed using the multispectral camera. Multi-linear regression (MLR) was then applied to derive the relationship between Rrs of each wavelength observed using the multispectral sensor on the UAV and the in-situ CHL. As a result of applying MLR, the correlation and root mean square error (RMSE) between the remotely sensed and in-situ CHLs were 0.94 and ~0.8 μg L−1, respectively; these results show a higher correlation coefficient and lower RMSE than those of other, previous studies. The newly derived algorithm for the CHL estimation enables us to survey 2D CHL images at high temporal and spatial resolutions in extremely turbid coastal oceans.

scholarly journals Limiting Accuracy of Height Measurement for a Precision Radar Altimeter in a Low Altitude Flying Vehicle above the Sea Surface

2021 ◽  
Vol 13 (14) ◽  
pp. 2660
Author(s):  
Aleksandr I. Baskakov ◽  
Alexey A. Komarov ◽  
Anna V. Ruban ◽  
Min-Ho Ka
Keyword(s):  
Measurement Errors ◽  
Large Scale ◽  
Tracking System ◽  
Sea Surface ◽  
Radar Altimeter ◽  
Height Measurement ◽  
Estimation Errors ◽  
Height Estimation ◽  
Deterministic Function ◽  
Low Altitude

This study presents mathematical analysis and numerical modeling for the estimation of measurement errors of height estimation over the sea surface for a precision radar altimeter installed in a low altitude flying vehicle. Reflective properties of the electromagnetic signals from the sea surface are determined by the local backscattering patterns of the sea surface illuminated. The height estimation of the flying vehicle from the received echo signals at the output of its tracking system is the sum of three factors: the first factor is the height to the average sea level the second is the bias of the estimation of the height, which is time-varying and depends on the slope of large-scale roughness; the third is the terms related to the surface topography. For the calculation of the estimation errors of the height measurement of a low altitude precision radar altimeter, a reasonable approximation of the large roughness of the sea surface by a deterministic function is necessary. In this study, we performed the derivation of the estimation function and the analysis of the limiting accuracy of the height measurement using the calculation of the estimation errors in spectral domain method describing the large-scale sea surface roughness. The results obtained for the limiting accuracy of a flying vehicle at low altitude above the sea surface, allows to obtain reasonable system parameters minimizing height errors of the flight altitude.

scholarly journals TEST SYSTEM OF ECOLOGICAL MONITORING OF FILM POLLUTIONS IN THE GORKOVSKY RESERVOIR

2020 ◽  
pp. 11-19
Author(s):  
Alexey V. Ermoshkin ◽  
Ivan A. Kapustin ◽  
Alexandr A. Molkov ◽  
Evgeny I. Poplavsky ◽  
Nikita S. Rusakov
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
Physical Dependence ◽  
Data Modeling ◽  
Ecological Monitoring ◽  
Test System ◽  
Sea Surface ◽  
X Band ◽  
Coherent Radar ◽  
Model Approach

The article considers the issue of creating a system of environmental monitoring of film pollutions in the Gorky reservoir. A combination of the well-known model approach for calculating the drift trajectories of passive particles on the sea surface with the data of remote sensing, which provide primary detection of a spill of pollutants in the scanned area, is proposed. X-band digital coherent radar was the source of remote data. Modeling was based on the results of measurements of current velocities and the physical dependence of the slick drift. As a result of comprehensive studies, the developed system has demonstrated operability for detecting and predicting the spread of film pollution in the Gorky reservoir.

Seismic Damage Information Extent about the Buildings Based on Low-Altitude Remote Sensing Images of Mianzu Quake-Stricken Areas

2011 ◽  
Vol 105-107 ◽  
pp. 1889-1893 ◽  
Author(s):  
Tao Zeng ◽  
Wu Nian Yang ◽  
Xiao Dong Li
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Large Scale ◽  
Object Oriented ◽  
Image Objects ◽  
Sensing Platforms ◽  
Building Information ◽  
Low Altitude ◽  
True Color

Using large-scale true-color images of Zundao Town of Mianzhu quake-hit areas gained form low-altitude remote sensing platforms, the author processed those images based on the 3S and image processing technology, then to presents a new object-oriented scheme of damaged building information extraction in the 5.12 Earthquake from the high-resolution low-altitude remote sensing in Zundao Town of Mianzhu. The scheme has two steps. The first step is that the whole imagery was segmented into image objects which do not intersect mutually. The second step is that to extract damaged buildings and undamaged ones with the features used to classify, like spectral, texture, shape and context. The experimental results indicate that the fast image processing method of UAV and the new object-oriented information extraction technique have high accuracy compared to the traditional classification methods and have a great application potential.

High Temporal and Spatial Resolutions of Sea Surface Current from Low-Altitude Remote Sensing

2019 ◽  
Vol 90 (sp1) ◽  
pp. 282 ◽  
Author(s):  
Dawoon Jung ◽  
Jong-Seok Lee ◽  
Ji-Yeon Baek ◽  
Jungho Nam ◽  
Young-Heon Jo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Current ◽  
Sea Surface ◽  
Temporal And Spatial ◽  
Low Altitude

DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Jose Antonio Moreira Lima
Keyword(s):  
Atlantic Ocean ◽  
Large Scale ◽  
North Pacific Ocean ◽  
Integrated Approach ◽  
Sea Surface ◽  
Global Ocean ◽  
Height Anomaly ◽  
The North ◽  
Observation Network ◽  
Ocean Forecasting

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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