Applying target shadow models for SAR ATR

Scott Papson, Ram Mohan Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSignal Processing, Sensor Fusion, and Target Recognition XVI
DOIs
StatePublished - Nov 15 2007
EventSignal Processing, Sensor Fusion, and Target Recognition XVI - Orlando, FL, United States
Duration: Apr 9 2007Apr 11 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6567
ISSN (Print)0277-786X

Other

OtherSignal Processing, Sensor Fusion, and Target Recognition XVI
CountryUnited States
CityOrlando, FL
Period4/9/074/11/07

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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