Abstract

This paper proposes a set of uncommonly rich feature representations for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. The proposed novel feature representations capture both the spatial and spectral properties of a target in a unified framework, while simultaneously offering discrimination and robustness to aspect variations. Specifically, the proposed features are mainly derived from the ideas of the monogenic signal and polar mapping. The applicability of the monogenic signal within the field of SAR target recognition is demonstrated by its capability of capturing both the broad spectral information and spatial localization with compact support. Further, to reduce the influence of inevitable variations due to aspect changes in SAR images, the monogenic components are transformed from Cartesian to polar coordinates through polar mapping. Additionally, a new target-shadow feature is also presented to compensate for the important discriminative information about target geometry, which exists in the shadow area. Finally, the proposed features are jointly considered into a unified multiple kernel learning framework for target recognition. Experiments on the moving and stationary target acquisition and recognition (MSTAR) public dataset demonstrate the strength and applicability of the proposed representations to SAR ATR. Moreover, it is also shown that overall high recognition accuracy can be obtained by the established unified framework.

© 2016 Optical Society of America

Full Article  |  PDF Article
More Like This
Stability and sensitivity of topographic features for synthetic aperture radar target characterization

Reuven Meth and Rama Chellappa
J. Opt. Soc. Am. A 16(2) 396-413 (1999)

Nonlinear techniques in optical synthetic aperture radar image generation and target recognition

Samuel Weaver and Kelvin Wagner
Appl. Opt. 34(20) 3981-3996 (1995)

Multi-class remote sensing object recognition based on discriminative sparse representation

Xin Wang, Siqiu Shen, Chen Ning, Fengchen Huang, and Hongmin Gao
Appl. Opt. 55(6) 1381-1394 (2016)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (11)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (4)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (16)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription