Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

被引：0
|
作者：
J. Del Rio Vera
E. Coiras
J. Groen
B. Evans
机构：
[1] NATO Undersea Research Centre (NURC),
[2] ESRIN,undefined
[3] European Space Agency (ESA),undefined
来源：
EURASIP Journal on Advances in Signal Processing | / 2009卷
关键词：
Feature Extraction; Classification Accuracy; Quantum Information; Sonar; Geometrical Feature;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a new supervised classification approach for automated target recognition (ATR) in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.
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