(Automatic) target detection in synthetic aperture radar imagery via terrain recognition

Surveillance of large areas of the Earth's surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a need to process these images with automatic target detection (ATD) algorithms. Typically the targets being searched fo...

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Bibliographic Details
Main Authors: Paget, R., Homer, J., Crisp, D.
Format: Conference Proceeding
Language:English
Subjects:
Australia
Geoscience
Image recognition
Image resolution
Markov random fields
Object detection
Satellites
Surveillance
Synthetic aperture radar
Target recognition
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Summary:Surveillance of large areas of the Earth's surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a need to process these images with automatic target detection (ATD) algorithms. Typically the targets being searched for are vehicles or small vessels, which occupy only a few resolution cells. Simple thresholding is usually inadequate for detection due to the high amount of noise in the images. Often the background has a discernible texture, and one form of detection is to search for anomalies in the texture caused by the presence of the target pixels. To perform this task a texture model must be able to model a variety of textures at run time, and also model these textures well enough to detect anomalies. We accomplish this with our multiscale nonparametric Markov random field (MRF) texture model.
DOI:10.1109/ICIP.2001.958049