Accession Number:

ADA465758

Title:

Robust Recognition of Ship Types from an Infrared Silhouette

Descriptive Note:

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Report Date:

2004-06-01

Pagination or Media Count:

19.0

Abstract:

Accurate identification of unknown contacts crucial in military intelligence. Automated systems that quickly and accurately determine the identity of a contact could be a benefit in backing up electronic-signals identification methods. This work reports two experimental systems for ship classification from infrared FLIR images. In an edge-histogram approach, we used the histogram of the binned distribution of observed straight edge segments of the ship image. Some simple tests had a classification success rate of 80 on silhouettes. In a more comprehensive neural network approach, we calculated scale-invariant moments of a silhouette and used them as input to a neural network. We trained the network on several thousand perspectives of a wire-frame model of the outline of each of five ship classes. We obtained 70 accuracy with detailed tested on real infrared images but performance was more robust than with the edge-histogram approach.

Subject Categories:

  • Marine Engineering
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE