Abstract
Synthetic aperture radar (SAR) is one of the most widely used remote sensing monitoring methods for large-scale marine activities. Due to the influence of speckle noise, sea clutter and complex environment, ship detection of SAR images is still a challenging task. Based on the multi-layer selective cognition characteristics of the human visual system, we propose a ship target detection algorithm based on region growing and multi-scale saliency. First, the layered rough-fine land-sea segmentation is used to remove the effect of land scattering. Second, the non-subsampled Laplacian pyramid (NSLP) filter is applied to decompose the image at different scales. Then, the saliency region of the transformed coefficients is extracted by spectral residual (SR). And the constant false alarm rate (CFAR) algorithm is used to further filter the false alarm and extract target more accurately. Finally, saliency sub-images of different scales are fused to get the final detection results. Experimental results show that the algorithm not only effectively suppresses the influence of land and sea clutter, but also can improve the detection rate.
The first author is a student.
This research was funded by the Fundamental Research Funds for the Central Universities No. 2020YJS033; Natural Science Foundation of China under grant 61401308 and 61572063; Natural Science Foundation of Hebei Province under grant F2016201142, F2020201025 and F2018210148; Science research project of Hebei Province under grant BJ2020004; Opening Foundation of Machine vision Engineering Research Center of Hebei Province under grant 2018HBMV02.
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Hu, Q., Hu, S., Liu, S. (2020). Ship Detection in SAR Images Based on Region Growing and Multi-scale Saliency. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_10
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DOI: https://doi.org/10.1007/978-3-030-60633-6_10
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