Evaluation of CFAR and texture based target detection statistics on SAR imagery

In this work, we evaluated the effectiveness of synthetic aperture radar (SAR) target detection algorithms that consist of any number of combinations of three statistics which include two-parameter CFAR, variance, and extended fractal features. The performance of these algorithms were tested at vari...

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Bibliographic Details
Main Authors: Kaplan, L.M., Murenzi, R.
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Fractals
Image databases
Image recognition
Object detection
Pixel
Spatial databases
Statistics
Target recognition
Testing
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Description
Summary:In this work, we evaluated the effectiveness of synthetic aperture radar (SAR) target detection algorithms that consist of any number of combinations of three statistics which include two-parameter CFAR, variance, and extended fractal features. The performance of these algorithms were tested at various threshold settings over the public domain MSTAR database. This database contains one foot resolution X-band SAR imagery. Receiver-operating-characteristic (ROC) curves were generated for the seven resulting algorithms. The results indicate that the CFAR statistic is the least effective detection statistic.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1998.681569