Share Email Print
cover

Proceedings Paper

Applying target shadow models for SAR ATR
Author(s): Scott Papson; Ram M. Narayanan
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Recent work has suggested that target shadows in synthetic aperture radar (SAR) images can be used effectively to aid in target classification. The method outlined in this paper has four steps - segmentation, representation, modeling, and selection. Segmentation is the process by which a smooth, background-free representation of the target's shadow is extracted from an image chip. A chain code technique is then used to represent the shadow boundary. Hidden Markov modeling is applied to sets of chain codes for multiple targets to create a suitable bank of target representations. Finally, an ensemble framework is proposed for classification. The proposed model selection process searches for an optimal ensemble of models based on various target model configurations. A five target subset of the MSTAR database is used for testing. Since the shadow is a back-projection of the target profile, some aspect angles will contain more discriminatory information then others. Therefore, performance is investigated as a function of aspect angle. Additionally, the case of multiple target looks is considered. The capability of the shadow-only classifier to enhance more traditional classification techniques is examined.

Paper Details

Date Published: 7 May 2007
PDF: 10 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670H (7 May 2007); doi: 10.1117/12.720223
Show Author Affiliations
Scott Papson, The Pennsylvania State Univ. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top