scholarly journals Processing the results of space observation of the processed areas of Karaganda

2021 ◽  
Author(s):  
A. Alimzhanova ◽  
◽  
Kh. Kadylbekova ◽  
Keyword(s):  
Software Package ◽  
Radar Data ◽  
Dynamic Processes ◽  
Archival Data ◽  
Satellite Systems ◽  
Radar Images ◽  
Modern Methods ◽  
Radar Satellite

The article is devoted to the development of a method for tracking deformation in the underworked territories of Karaganda based on the data processed by radar images from the ENVISAT satellite. The article provides an overview of the use of modern radar satellite systems. The step-by-step search of archival data on the territory of Karaganda in the Eoli-sa program is described. The processing of radar images from the ENVISAT satellite for the period from 2003 to 2010 in the SARscape module of the ENVI software package is described in detail. Based on the processed data, graphs of dynamic processes were compiled. The analysis of the results of interferometric processing of radar data is performed. Traditional and modern methods of tracking the deformation of underworked territories are also analyzed.

scholarly journals Spaceborne radar imagery – An under-utilized source of information for humanitarian relief

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Andreas Braun Braun
Keyword(s):  
Radar Data ◽  
Humanitarian Relief ◽  
Humanitarian Action ◽  
Radar Images ◽  
Radar Imagery ◽  
Multi Temporal ◽  
Radar Satellite ◽  
Spaceborne Radar ◽  
Source Of Information ◽  
Humanitarian Work

This practical paper gives an overview about the widely unused potential of radar satellite imagery to assist humanitarian action. It briefly introduces the basic differences between optical and radar images and demonstrates the practical use of the latter in different settings based on their information content and their potential for multi-temporal analyses. It gives recommendations on further reading and closes with suggestions on the practical integration of radar data into humanitarian work.

The research study in the convergence of the results of processing radar images Sentinel-1A with field observations of agricultural sites

2019 ◽  
Vol 950 (8) ◽  
pp. 52-58
Author(s):  
D.V. Mozer ◽  
Е.L. Levin ◽  
A.K. Satbergenova
Keyword(s):  
Satellite Remote Sensing ◽  
Satellite Images ◽  
Research Study ◽  
High Accuracy ◽  
Agricultural Fields ◽  
Field Observations ◽  
Radar Images ◽  
The Earth ◽  
Radar Satellite ◽  
Modern Technologies

The manuscript discusses how to monitor the condition of seedlings on agricultural fields planted with winter wheat, fodder maize and areas of fir forest located in the Freudenstadt district of Baden-Wuerttemberg in Germany. To solve the range of agricultural problems , they often use modern technologies such as satellite remote sensing of the Earth. The paper displays the monitoring results of the Sentinel-1A radar satellites scenes, as well as visual spectrum imagery of field observations are presented when leaving directly to terrain segments. The processing deployed data chain, consisting of 11 Sentinel-1A scenes acquired in the timefrane from March to November 2018. Specifically, the SNAP Sentinel Toolboxes software was used to process the radar satellite images Sentinel-1А, the. Based on the the research outcomes the Committee of Agriculture of the Freudenstadt district is able to predict the yield amount with high accuracy due to good data convergence. According to the study, the following three important problems can be resolved by means of Sentinel-1A imagery

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

<p>Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., Béréziat, D., Brajard, J., Charantonis, A., & Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015</p>

scholarly journals Method for transforming symbolic radar marks of low-noticeable moving objects based on the Talbot effect

Radiotekhnika ◽  
2021 ◽  
pp. 129-137
Author(s):  
V. Zhyrnov ◽  
S. Solonskaya
Keyword(s):  
Moving Objects ◽  
Radar Data ◽  
Pulse Frequency ◽  
Asymptotic Equality ◽  
Talbot Effect ◽  
Radar Images ◽  
Frequency Code ◽  
Semantic Dependencies ◽  
Symbolic Images ◽  
Synchronous Pulse

In this paper a method to transform radar images of moving aerial objects with scintillating inter-period fluctuations, sometimes resulting to complete signal fading, using the Talbot effect is considered. These transformations are reduced to the establishment of a certain correspondence of the asymptotic equality of perception of visual images, arbitrarily changing in time and space, in the statement about the conditions of simple equality of perception of images of radar marks that have different frequencies of fluctuations. It is shown how this approach can be used to analyze radar data by transforming and smoothing scintillating signal fluctuations, invisible in the presence of interference, into visible symbolic images. First, to detect and recognize the aerial objects from the analysis of relations and functional (semantic) dependencies between attributes, second, to make a decision based on semantic components of symbolic radar images. The possibility of using such transformation to generate pulse-frequency code of fluctuations of the symbolic radar angel-echo images as an important characteristic for their recognition has been experimentally verified. Algorithms for generating symbolic images in asynchronous and synchronous pulse-frequency code are formulated. The symbolic image represented by such a code is considered as an additional feature for recognizing and filtering out natural interferences such as angel-echoes.

scholarly journals A 5-yr 40-km-Resolution Global Climatology of Superrefraction for Ground-Based Weather Radars

2009 ◽  
Vol 48 (1) ◽  
pp. 89-110 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
United States ◽  
Electromagnetic Waves ◽  
Vertical Gradient ◽  
Radar Data ◽  
The United States ◽  
Horizontal Resolution ◽  
Trade Wind ◽  
Late Afternoon ◽  
The Balkans ◽  
Radar Images

Abstract The propagation of electromagnetic waves emitted from ground-based meteorological radars is determined by the stratification of the atmosphere. In extreme superrefractive situations characterized by strong temperature inversions or strong vertical gradients of moisture, the radar beam can be deflected toward the ground (ducting or trapping). This phenomenon often results in spurious returned echoes and misinterpretation of radar images such as erroneous precipitation detection. In this work, a 5-yr global climatology of the frequency of superrefractive and ducting conditions and of trapping-layer base height has been produced using refractivity computations from ECMWF temperature, moisture, and pressure analyses at a 40-km horizontal resolution. The aim of this climatology is to better document how frequent such events are, which is a prerequisite for fully benefiting from radar data information for the multiple purposes of model validation, precipitation analysis, and data assimilation. First, the main climatological features are summarized for the whole globe: high- and midlatitude oceans seldom experience superrefraction or ducting whereas tropical oceans are strongly affected, especially in regions where the trade wind inversion is intense and lying near the surface. Over land, seasonal averages of superrefraction (ducting) frequencies reach 80% (40%) over tropical moist areas year-round but remain below 40% (15%) in most other regions. A particular focus is then laid on Europe and the United States, where extensive precipitation radar networks already exist. Seasonal statistics exhibit a pronounced diurnal cycle of ducting occurrences, with averaged frequencies peaking at 60% in summer late afternoon over the eastern half of the United States, the Balkans, and the Po Valley but no ducts by midday. Similarly high ducting frequencies are found over the southwestern coast of the United States at night. A potentially strong reduction of ducting occurrences with increased radar height (especially in midlatitude summer late afternoon) is evidenced by initiating refractivity vertical gradient computations from either the lowest or the second lowest model level. However, installing radar on tall towers also brings other problems, such as a possible amplification of sidelobe clutter echoes.

Active Microwave Systems for Monitoring Drought Stress

2005 ◽  
Author(s):  
Anne M. Smith
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Value Added ◽  
Agricultural Drought ◽  
Sensor System ◽  
Spatial And Temporal Scales ◽  
Satellite Systems ◽  
Radio Detection ◽  
Vegetation Water ◽  
Radar Satellite

Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.

scholarly journals Detecting and Tracking Communal Bird Roosts in Weather Radar Data

2020 ◽  
Vol 34 (01) ◽  
pp. 378-385
Author(s):  
Zezhou Cheng ◽  
Saadia Gabriel ◽  
Pankaj Bhambhani ◽  
Daniel Sheldon ◽  
Subhransu Maji ◽  
...  
Keyword(s):  
Latent Variable ◽  
Weather Radar ◽  
Radar Data ◽  
Learning System ◽  
Variable Model ◽  
Detection Model ◽  
Radar Images ◽  
Biological Phenomena ◽  
The Us ◽  
Spatio Temporal

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

Navigation by Computer Processing of Marine Radar Images

1985 ◽  
Vol 38 (3) ◽  
pp. 375-383 ◽  
Author(s):  
G. L. Austin ◽  
A. Bellon ◽  
M. Riley ◽  
E. Ballantyne
Keyword(s):  
Radar Data ◽  
Computer Processing ◽  
Pattern Recognition Technique ◽  
Radar Images ◽  
Well Yield ◽  
Radar Imagery ◽  
On Line ◽  
Marine Radar ◽  
Recognition Technique ◽  
Better Than

The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.

scholarly journals NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 290 ◽  
Author(s):  
Rubel ◽  
Lukin ◽  
Rubel ◽  
Egiazarian
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Real Life ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Quality Metrics ◽  
Radar Images ◽  
Filtering Efficiency ◽  
Trained Neural Network ◽  
Synthetic Aperture Radars

Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics.

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