DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

Xin Yi He; Bo Zhao; Shu Run Tan; Xiao Yang Zhou; Zhong Jin Jiang; Tie Jun Cui

doi:10.1117/12.832853

30 October 2009 DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

Xin Yi He, Bo Zhao, Shu Run Tan, Xiao Yang Zhou, Zhong Jin Jiang, Tie Jun Cui

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74951N (2009) https://doi.org/10.1117/12.832853
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

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Xin Yi He, Bo Zhao, Shu Run Tan, Xiao Yang Zhou, Zhong Jin Jiang, and Tie Jun Cui "DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74951N (30 October 2009); https://doi.org/10.1117/12.832853

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KEYWORDS

Synthetic aperture radar

Automatic target recognition

Target recognition

Fractal analysis

Scattering

Detection and tracking algorithms

Image processing algorithms and systems

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