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Proceedings Paper

Ship detection in presence of sea clutter from temporal sequences of navigation radar images
Author(s): Xianwen Ding; Weigen Huang; Changbao Zhou; Peng Chen; Bin Liu
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Paper Abstract

This work presents a method to suppress the sea clutter for radar images acquired from ordinary navigation radar sensors, which are incoherent radars working in X-band and horizontal polarization. The proposed method considers short temporal sequences of consecutive navigation radar images. This method, which is based on rotation-to-rotation correlation and the variation of sea clutter response with range, can be described as follows. 1) To cumulate the k (k>1) temporally consecutive images. 2) To fit the variation of the sea clutter intensity with range for every scan line of the cumulative image and to subtract the fitted sea clutter intensity from the cumulative image for every pixel. 3) To calculate the threshold value of detection by applying the Constant False Alarm (CFAR) model and the Probabilistic Neural Networks (PNN) model. 4) To threshold the resulting image with the obtained threshold. 5) To remove the false alarm by utilizing the flood fill algorithm to determine the connected area size of any probable target in the binary image. Temporal sequences of navigation radar images were used to test the performance of the proposed method. The results obtained show that the proposed method is able to reduce significantly the sea clutter from the radar images and detect efficiently a ship embedded in the sea clutter. The detection precision is provided according to the experimental results.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954I (30 October 2009); doi: 10.1117/12.832882
Show Author Affiliations
Xianwen Ding, Second Institute of Oceanography, State Oceanic Administration (China)
Weigen Huang, Second Institute of Oceanography, State Oceanic Administration (China)
Changbao Zhou, Second Institute of Oceanography, State Oceanic Administration (China)
Peng Chen, Second Institute of Oceanography, State Oceanic Administration (China)
Bin Liu, Second Institute of Oceanography, State Oceanic Administration (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis

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