This research compares the performance improvements of image-based sonar target classification algorithms when they are adapted to changing clutter environments. The distribution of seabed pixels in the sonar imagery is modeled as a correlated, K-distributed random variable allowing for a quantitative representation of seabed environments in the various testing scenarios. Parameterized environments comprising various target-like seabed textures are generated synthetically and used to examine adaptive classification performance. Results demonstrate that optimizing classifier parameters respective to specific environments improves overall classification performance compared to optimizing classifier parameters against a pooled dataset that includes all possible environments.
In Situ Adaptive Feature Extraction for Underwater Target Classification
01.10.2007
614210 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
An adaptive underwater target classification system
Tema Archiv | 1999
|A New Adaptive Immune Feature Selection Algorithm for Underwater Acoustic Target Classification
British Library Online Contents | 2011
|Adaptive classification of underwater transients
Tema Archiv | 1989
|Autonomous underwater robot propeller fault feature extraction method
Europäisches Patentamt | 2020
|Underwater target classification using wavelet packets and neural networks
Tema Archiv | 1998
|