Comparison of possibilistic fuzzy local information C-means and possibilistic K-nearest neighbors for synthetic aperture sonar image segmentation-Reference-Cited by-同舟云学术

Comparison of possibilistic fuzzy local information C-means and possibilistic K-nearest neighbors for synthetic aperture sonar image segmentation

Published:2019-05-10 Issue: Volume: Page:
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Container-title:Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
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Author:

Peeples Joshua,Cook Matthew,Suen Daniel,Zare Alina,Keller James

Publisher

SPIE

Reference28 articles.

1. Automatic Target Recognition: State of the Art Survey

2. Survey of neural network technology for automatic target recognition

3. Automatic target recognition in synthetic aperture sonar images based on geometrical feature extraction;Del Rio Vera;EURASIP Journal on Advances in Signal Processing,2009

4. Exploiting Environmental Information for Improved Underwater Target Classification in Sonar Imagery

5. Model-based sea mine classification with synthetic aperture sonar

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