An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

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ID: 110162
2020
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Abstract
With the improvement of image resolution in synthetic aperture radars (SARs), sea clutter characteristics become more complex, which poses new challenges to traditional ship target detection missions. In this paper, to detect ship targets quickly and efficiently in a complex background, we propose an adaptive hierarchical detection method based on a coarse-to-fine mechanism. This method constructs a new visual attention mechanism to strengthen ship targets and obtain the candidate targets adaptively by the means dichotomy method. On this basis, the precise detection results of the targets are obtained using the speed block kernel density estimation method, which maintains constant false alarm characteristics. Compared with existing methods, the adaptive hierarchical detection method has simple, fast, and accurate characteristics. Experiments based on GF-III satellite and airborne SAR datasets are presented to demonstrate the effectiveness of the proposed method.
Reference Key
liang2020remotean Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yi Liang;Kun Sun;Yugui Zeng;Guofei Li;Mengdao Xing;Liang, Yi;Sun, Kun;Zeng, Yugui;Li, Guofei;Xing, Mengdao;
Journal remote sensing
Year 2020
DOI
10.3390/rs12020303
URL
https://www.mdpi.com/2072-4292/12/2/303##abstractc
Keywords
synthetic aperture radar kernel density estimation ship detection saliency method superpixel segmentation synthetic aperture radar kernel density estimation ship detection saliency method superpixel segmentation

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