Enhanced synthetic aperture radar automatic target recognition method based on novel features.
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.