New rotation-invariant features for radar target recognition

Spoelstra, Jacob; Botha, Elizabeth C.

doi:10.1016/s0165-1684(97)86991-5

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…For identifying and tracking targets in radar imagery, current approaches use characteristics of the radar signature [20], models of trajectory [21] and also scatter from between targets [22]. Yeary et al [20] use an iterative particle filter approach for tracking of tornado related features.…”

Section: Background: Feature Selection and Extractionmentioning

confidence: 99%

Extracting Quantitative Information on Coastal Ice Dynamics and Ice Hazard Events From Marine Radar Digital Imagery

Rohith

Jones

Eicken

et al. 2013

IEEE Trans. Geosci. Remote Sensing

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Marine radars have been employed to gather data in applications that require near-continuous monitoring and tracking of objects over a wide area from a single viewpoint, independent of weather and light conditions. However, little attention has been paid toward utilizing such systems for the study of long-term phenomena and detecting anomalous environmental events or hazards that may occur infrequently but have potentially significant impacts on coastal populations. In this paper, we concentrate on tracking features in seasonally ice-covered Arctic coastal ocean environments. We have developed tools for automated analysis of ground-based radar images of landfast ice and moving sea ice to extract ice-floe trajectories and velocity fields, delineate the boundary of stable landfast ice, detect events relevant to coastal populations and identify surface vessels. We employ dense and feature-based optical flow approaches to compute motion fields from the images, active contours for delineation of stable landfast ice, and Hidden Markov Models for machine learning based event detection. We present results from the analysis of sample images jointly with a quantitative evaluation of algorithm performance relative to operator-based assessments.

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Section: Background: Feature Selection and Extractionmentioning

confidence: 99%

Extracting Quantitative Information on Coastal Ice Dynamics and Ice Hazard Events From Marine Radar Digital Imagery

Rohith

Jones

Eicken

et al. 2013

IEEE Trans. Geosci. Remote Sensing

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“…Classification of radar targets via 1-D down-range images has been studied using correlation filters [5,9], multiple bounce features [11], bispectrum features [6] and neural network classifiers [2,8]. A promising field is the use of 2-D image formation via inverse synthetic aperture radar to classify radar targets [4].…”

Section: Introductionmentioning

confidence: 99%

Relative performance of correlation-based and feature-based classifiers of aircraft using radar range profiles

Nieuwoudt

Botha²

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170)

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A surveillance system which automatically classifies aircraft can be very useful, especially with the problem of congestion at large airports. This paper evaluates the performance of correlation-and feature-based classifiers on a set of four simulated radar targets over a wide range of target orientations. Experiments are performed for a range of radar bandwidths in order to determine the effect of radar bandwidth on the relative classification performance. Only 1-D radar range profiles are considered since it is assumed that the aircraft are classified using few profiles and the orientation of the aircraft in each profile is known only approximately. The results suggest that feature-based classifiers outperform correlation-based classifiers and that classification performance is highly dependent on the orientation of the aircraft, but that accurate classification of approaching aircraft is possible.

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Relative performance of correlation-based and neural-network-based classifiers of aircraft using synthetic radar range profiles

Nieuwoudt

Botha

2000

Opt. Eng

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New rotation-invariant features for radar target recognition

Cited by 3 publications

References 5 publications

Extracting Quantitative Information on Coastal Ice Dynamics and Ice Hazard Events From Marine Radar Digital Imagery

Extracting Quantitative Information on Coastal Ice Dynamics and Ice Hazard Events From Marine Radar Digital Imagery

Relative performance of correlation-based and feature-based classifiers of aircraft using radar range profiles

Relative performance of correlation-based and neural-network-based classifiers of aircraft using synthetic radar range profiles

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