scholarly journals A mathematical perspective on radar interferometry

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Mikhail Gilman ◽  
Semyon Tsynkov
Keyword(s):  
Radar Data ◽  
Radar Interferometry ◽  
Point Scatterer ◽  
Imaging Geometry ◽  
Additional Information ◽  
Radar Images ◽  
Sensing Technology ◽  
The Common ◽  
Isolated Point ◽  
Imaging Conditions

<p style='text-indent:20px;'>Radar interferometry is an advanced remote sensing technology that utilizes complex phases of two or more radar images of the same target taken at slightly different imaging conditions and/or different times. Its goal is to derive additional information about the target, such as elevation. While this kind of task requires centimeter-level accuracy, the interaction of radar signals with the target, as well as the lack of precision in antenna position and other disturbances, generate ambiguities in the image phase that are orders of magnitude larger than the effect of interest.</p><p style='text-indent:20px;'>Yet the common exposition of radar interferometry in the literature often skips such topics. This may lead to unrealistic requirements for the accuracy of determining the parameters of imaging geometry, unachievable precision of image co-registration, etc. To address these deficiencies, in the current work we analyze the problem of interferometric height reconstruction and provide a careful and detailed account of all the assumptions and requirements to the imaging geometry and data processing needed for a successful extraction of height information from the radar data. We employ two most popular scattering models for radar targets: an isolated point scatterer and delta-correlated extended scatterer, and highlight the similarities and differences between them.</p>

scholarly journals Radar Data for Identifying the Characteristics of Tropical Forest Stands

2016 ◽  
Vol 30 (1) ◽  
pp. 69
Author(s):  
Agus Wuryanta
Keyword(s):  
Canopy Cover ◽  
Basal Area ◽  
Window Size ◽  
Speckle Noise ◽  
Radar Data ◽  
Forest Stand ◽  
Radar Backscatter ◽  
Radar Images ◽  
Sensing Technology ◽  
The Relationship

Radar is one of remote sensing technology which utilizes active electromagnetic energy and are able to provide information about the characteristics of forest stand. This study utilized JERS-1 and ERS-1 radar images to analyze the relationship between the radar backscatter and forest stand characteristics such as Diameter Breast Height (DBH), basal area, and canopy cover. This research was conducted in Jambi Province, Bungo Tebo District, Sumatra, Indonesia. The research site covered the forest concession, Suku Anak Dalam, the area adjacent to Pelepat and Batang Tebo River, and Kuamang Kuning village. Gamma Map Filter with 7 x 7 window size was applied to reduce speckle noise of the SAR images (ERS-1 and JERS-1). This study found out the positive significant correlation between basal area and DBH with JERS-1 radar backscatter (i.e., r = 0.75 and r = 0.70), while ERS-1 radar backscatter has correlation (r = 0.64) with the canopy cover.

Neural network processing of radar scanning data array

2021 ◽  
Author(s):  
V.E. Dmitriyev ◽  
D.V. Popov ◽  
V.A. Shakhnov
Keyword(s):  
Spatial Perception ◽  
Digital Processing ◽  
Radar Data ◽  
Information Value ◽  
Data Array ◽  
Additional Information ◽  
Distribution Of Values ◽  
The Difference ◽  
Radar Data Processing ◽  
Made In

This article deals with the digital processing of a matrix radar image. The information received from the radar scanner needs to be transformed to enable visual perception. The article describes the main methods of digital processing of matrix data, presents the images transformed by them. The aim of the article was the development of a radar data processing algorithm that identifies the contours and edges of examined objects. The authors propose an algorithm for isolating the geometric structure of the scanned area. The difference between the processing method and the known analogues is based on the nature of the change in the values of the array being processed and consists in the double operation of extracting the gradient of the distribution of values. The software implementation of the algorithm is made in C++ using methods from an open library of computer vision. The efficiency of the algorithm was estimated based on comparison with the algorithms for determining edges based on linear filtering and neural networks. The results of the work can be used to create software for mobile short-range radar devices. Imaging from object boundaries and their edges provides spatial perception of the image by the operator, and free areas are available for rendering additional information. This solution allows you to combine scanning devices and thereby increase the information value of the result.

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

&lt;p&gt;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., B&amp;#233;r&amp;#233;ziat, D., Brajard, J., Charantonis, A., &amp; 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&lt;/p&gt;

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.

scholarly journals Design of an experimental four-camera setup for enhanced 3D surface reconstruction in microsurgery

2017 ◽  
Vol 3 (2) ◽  
pp. 539-542 ◽  
Author(s):  
Christian Marzi ◽  
Andreas Wachter ◽  
Werner Nahm
Keyword(s):  
3D Reconstruction ◽  
Reconstruction Algorithms ◽  
Camera System ◽  
Additional Information ◽  
Wide Range ◽  
Main Disadvantage ◽  
The Common ◽  
Visualization Systems ◽  
Compact Design ◽  
The Impact

AbstractFuture fully digital surgical visualization systems enable a wide range of new options. Caused by optomechanical limitations a main disadvantage of today’s surgical microscopes is their incapability of providing arbitrary perspectives to more than two observers. In a fully digital microscopic system, multiple arbitrary views can be generated from a 3D reconstruction. Modern surgical microscopes allow replacing the eyepieces by cameras in order to record stereoscopic videos. A reconstruction from these videos can only contain the amount of detail the recording camera system gathers from the scene. Therefore, covered surfaces can result in a faulty reconstruction for deviating stereoscopic perspectives. By adding cameras recording the object from different angles, additional information of the scene is acquired, allowing to improve the reconstruction. Our approach is to use a fixed four-camera setup as a front-end system to capture enhanced 3D topography of a pseudo-surgical scene. This experimental setup would provide images for the reconstruction algorithms and generation of multiple observing stereo perspectives. The concept of the designed setup is based on the common main objective (CMO) principle of current surgical microscopes. These systems are well established and optically mature. Furthermore, the CMO principle allows a more compact design and a lowered effort in calibration than cameras with separate optics. Behind the CMO four pupils separate the four channels which are recorded by one camera each. The designed system captures an area of approximately 28mm × 28mm with four cameras. Thus, allowing to process images of 6 different stereo perspectives. In order to verify the setup, it is modelled in silico. It can be used in further studies to test algorithms for 3D reconstruction from up to four perspectives and provide information about the impact of additionally recorded perspectives on the enhancement of a reconstruction.

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.

Oxford Textbook of Old Age Psychiatry

2013 ◽  
Keyword(s):  
Decision Making ◽  
Palliative Care ◽  
Old Age ◽  
Mental Capacity ◽  
Global Approach ◽  
Additional Information ◽  
Memory Clinics ◽  
Old Age Psychiatry ◽  
The Common ◽  
Ethics Of Caring

Taking a global approach by highlighting both the common burdens and the differences in management from country to country, The Oxford Textbook of Old Age Psychiatry, Second Edition includes information on all the latest improvements and changes in the field. New chapters are included to reflect the development of old age care; covering palliative care, the ethics of caring, and living and dying with dementia. Existing chapters have also been revised and updated throughout and additional information is included on brain stimulation therapies, memory clinics and services, and capacity, which now includes all mental capacity and decision making.

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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