scholarly journals A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

2016 ◽  
Author(s):  
Ramakalavathi Marapareddy ◽  
James V. Aanstoos ◽  
Nicolas H. Younan
Keyword(s):  
Synthetic Aperture Radar ◽  
Mississippi River ◽  
Slope Instability ◽  
Synthetic Aperture ◽  
Detection Accuracy ◽  
Radar Imagery ◽  
Lower Mississippi River ◽  
Aerial Vehicle ◽  
Sar Imagery ◽  
Aperture Radar

The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.

scholarly journals Refocusing of Multiple Moving Targets Based on the Joint Sparse Processing of One Channel Synthetic Aperture Radar Imagery Patches

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1215 ◽  
Author(s):  
Xin Wang ◽  
Ling Qiao
Keyword(s):  
Synthetic Aperture Radar ◽  
Moving Objects ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Moving Targets ◽  
Lp Norm ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Norm Constraint ◽  
Aperture Radar

A sparse-based refocusing methodology for multiple slow-moving targets (MTs) located inside strong clutter regions is proposed in this paper. The defocused regions of MTs in synthetic aperture radar (SAR) imagery were utilized here instead of the whole original radar data. A joint radar projection operator for the static and moving objects was formulated and employed to construct an optimization problem. The Lp norm constraint was utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse imaging processing, the energy of strong static targets could be suppressed significantly in the reconstructed MT imagery. The static scene imagery could be derived simultaneously without the defocused MT. Finally, numerical simulations were used verify the validity and robustness of the proposed methodology.

scholarly journals ANALYSES OF MULTI-YEAR SYNTHETIC APERTURE RADAR IMAGERY OF DRY-FALLEN INTERTIDAL FLATS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 941-947 ◽  
Author(s):  
M. Gade ◽  
S. Melchionna ◽  
L. Kemme
Keyword(s):  
Synthetic Aperture Radar ◽  
Morphological Changes ◽  
Temporal Analysis ◽  
Synthetic Aperture ◽  
Mussel Beds ◽  
Intertidal Flats ◽  
Show Evidence ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Aperture Radar

We analyzed a great deal of high-resolution Synthetic Aperture Radar (SAR) data of dry-fallen intertidal flats in the German Wadden Sea with respect to the imaging of sediments, macrophytes, and mussels. TerraSAR-X and Radarsat-2 images of five test areas along the German North Sea coast acquired between 2008 and 2013 form the basis for the present investigation and are used to demonstrate that pairs of SAR images, if combined through basic algebraic operations, can already provide useful indicators for morphological changes and for bivalve (oyster and mussel) beds. Depending on the type of sediment, but also on the water level and on environmental conditions (wind speed) exposed sediments may show up on SAR imagery as areas of enhanced, or reduced, radar backscattering. The (multi-temporal) analysis of series of such images allows for the detection of mussel beds, and our results show evidence that also single-acquisition, multi-polarization SAR imagery can be used for that purpose.

scholarly journals Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2532 ◽  
Author(s):  
Edoardo Nemni ◽  
Joseph Bullock ◽  
Samir Belabbes ◽  
Lars Bromley
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Synthetic Aperture ◽  
Open Source Data ◽  
Wide Range ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Time Required ◽  
Aperture Radar

Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring.

scholarly journals Application for Displaying Synthetic Aperture Radar Imagery in Real-time Implementation and Results

2015 ◽  
Vol 61 (4) ◽  
pp. 345-350
Author(s):  
Krzysztof Borowiec
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Synthetic Aperture ◽  
Processing Application ◽  
Time Operation ◽  
Real Time Operation ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery ◽  
Sar Imagery ◽  
Aperture Radar

Abstract The paper presents implementation and results of the application for displaying SAR (Synthetic Aperture Radar) imagery operating in real-time. The application performs SAR imagery formation and displays results in real-time after receiving of preprocessed data via an SAR processing application. The application was used in SARape (Synthetic Aperture Radar for all weather penetrating UAV application) project founded by the European Defence Agency. The real-time operation is achieved thanks to implementation based on multithreading.

Applications of Statistical Directional Characteristics in Synthetic Aperture Radar Imagery Interpretation

2012 ◽  
Vol 239-240 ◽  
pp. 1269-1273
Author(s):  
Si Chen ◽  
Dong Ya Wang ◽  
Jian Yang
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Synthetic Aperture ◽  
Line Detection ◽  
Directional Characteristics ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery ◽  
Sar Imagery ◽  
Aperture Radar

The concept of Statistical Directional Characteristics (SDCs) is introduced in this paper. The applications of SDCs in Synthetic Aperture Radar (SAR) imagery interpretation are discussed. Two applications, i.e., target recognition and line detection, are raised to demonstrate the effectiveness of the SDCs-based methods. Both numerical and graphical results are presented. According to the experiment results, the SDCs-based methodology shows advantages both in robustness and in efficiency.

Sign in / Sign up

Export Citation Format

Share Document