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9 October 2018 Ship detection in SAR images based on Shearlet features
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Abstract
In consideration of the difficulty for ship detection in SAR images when the ship targets are blurred in speckle noise and clutter, a novel method for ship detection is proposed in this paper. In this approach, the discrete Shearlet features are adopted to capture the intrinsic geometrical features of ship target with discontinuities points and threshold detection method is used to get the ship targets. The SAR image is decomposed by Discrete Shearlet Transform (DST) in multiple scales to get different sub-bands, and the Shearlet coefficients of images are obtained in different sub-bands with different directions. As Shearlet coefficients of the target and the background have completely different performance properties in the high-frequency sub-bands in different directions. The Shearlet coefficients of the ship targets exhibit local maxima characteristics in high-frequency subbands in different directions, while the extreme values of Shearlet coefficients in the background are difficult to simultaneously appear in different directions. Experiments on SAR images with sea backgrounds and multiple ship targets situation have been performed. Comparison with wavelet and CFAR detection methods, the results demonstrate that the proposed method is competitive in detection rate and shape preservation.
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Zhuo Pan, Xueli Zhan, and Yanfei Wang "Ship detection in SAR images based on Shearlet features", Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107891I (9 October 2018); https://doi.org/10.1117/12.2325310
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