Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction
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 object...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2009-01-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/2009/109438 |
| Summary: | 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. |
|---|---|
| ISSN: | 1687-6172 1687-6180 |