Progressive neighbors pursuit for radar images classification

2021 ◽  
pp. 107194
Author(s):  
Shuyuan Yang ◽  
Guangying Xu ◽  
Huixiao Meng ◽  
Min Wang
Keyword(s):  
Radar Images

Methods for Blind Evaluation of Noise Variance in Multichannel Optical and Radar Images

2006 ◽  
Vol 65 (6) ◽  
pp. 527-556 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
N. N. Ponomarenko ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Noise Variance ◽  
Radar Images

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

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

scholarly journals Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3937
Author(s):  
Seungeon Song ◽  
Bongseok Kim ◽  
Sangdong Kim ◽  
Jonghun Lee
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Smart Home ◽  
Doppler Radar ◽  
Radar Images ◽  
Radar Signature ◽  
Memory Efficiency ◽  
High Compression ◽  
Value Decomposition ◽  
Deep Learning Model

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

scholarly journals Vulnerability Assessment of Buildings due to Land Subsidence Using InSAR Data in the Ancient Historical City of Pistoia (Italy)

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2749 ◽  
Author(s):  
Pablo Ezquerro ◽  
Matteo Del Soldato ◽  
Lorenzo Solari ◽  
Roberto Tomás ◽  
Federico Raspini ◽  
...  
Keyword(s):  
Fragility Curves ◽  
Central Italy ◽  
Sar Interferometry ◽  
Ground Subsidence ◽  
Public And Private ◽  
Radar Images ◽  
Spatial Deformation ◽  
Damage Probability ◽  
Medium Resolution ◽  
Vertical Displacements

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

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