Abstract
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.
Highlights
Underwater imaging has a wide range of applications, from pipeline inspection to seabed classification and underwater object classification [1]
automatic target recognition (ATR) systems based on side-scan have relied on a combination of radiometric and geometric features to identify objects of interest, focusing mainly on the spectral highlight response produced by the target and the configuration of the shadow cast on the seafloor
Receiver operating characteristic (ROC) curves were produced for each object class
Summary
Underwater imaging has a wide range of applications, from pipeline inspection to seabed classification and underwater object classification [1]. ATR systems based on side-scan have relied on a combination of radiometric and geometric features to identify objects of interest, focusing mainly on the spectral highlight response produced by the target and the configuration of the shadow cast on the seafloor. The target can have an arbitrary 3D shape, which is decomposed into facets each having their own travel time (or range R) and amplitude In this way for each facet, the corresponding pixel is determined, whilst the amplitude is computed using a Lambert’s scatter law in combination with the Rayleigh reflection coefficient that depends on the angle between facet normal vector and acoustic ray. We can see that the sea floor characteristics have an important effect on the visual classification, and will show that they have a large influence on classification with geometric features as well
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