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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Improving Maritime Domain Awareness using Neural Networks for Target of Interest Classification

Improving Maritime Domain Awareness using Neural Networks for Target of Interest Classification

Naval Postgraduate School
Penny Hill Press Inc

Paperback

Technology & EngineeringGeneral Political Science

ISBN10: 152273824X
ISBN13: 9781522738244
Publisher: St Paul Pr
Published: Dec 14 2015
Pages: 78
Weight: 0.45
Height: 0.16 Width: 8.50 Depth: 11.02
Language: English
Techniques for classifying maritime domain targets-of-interest within images are explored in this book. Geometric and photometric features within each image are extracted from processed images and are used to train a neural network. The trained neural network is tested with features of a known object. In the binary classification case, the neural network is used to determine whether a ship is present or not present in the image. In the multi-class and multi-level classification cases, the neural network is used to determine if the object belongs to one of four classes specified: warship, cargo ship, small boat, or other. The Hough transformation is used to identify and characterize linear patterns exhibited by objects in images. As an alternative to geometric and photometric features to classify targets-of-interest, these linear patterns are used to train a neural network. The performance of the neural network is then tested for binary, multi-class, and multi-level classification schemes. The development of neural-network-based techniques for automated target-of-interest classification is a significant result of this book.

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