Title :
Optimizing ship length estimates from ISAR images
Author :
McFadden, Frank E. ; Musman, Scott A.
Author_Institution :
Integrated Manage. Services Inc., Arlington, VA, USA
Abstract :
Ship length is extremely useful for ship classification; therefore, if it is possible to derive accurate ship length estimates from ISAR (inverse synthetic aperture radar) data, the classification and identification problem becomes much simpler. The paper demonstrates that it is possible to obtain extremely accurate measurements of ship length from ISAR images. The SAIC procedure used to produce ISAR images includes ship length estimates for each frame. Robust length estimates based on 2000 frames are accurate within +/- 10.5, but we show that they can be improved significantly by the use of a frame selection procedure based on a neural network, which achieves an accuracy of +/- 2.3
Keywords :
image classification; neural nets; parameter estimation; radar imaging; ships; synthetic aperture radar; ISAR images; SAIC procedure; inverse synthetic aperture radar; ship classification; ship length estimates; Accuracy; Displays; Focusing; Image sequences; Inverse synthetic aperture radar; Length measurement; Marine vehicles; Neural networks; Predictive models; Robustness;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857831
