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Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management
Open AccessArticle

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

National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
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Remote Sens. 2020, 12(2), 303; https://doi.org/10.3390/rs12020303
Received: 4 December 2019 / Revised: 10 January 2020 / Accepted: 15 January 2020 / Published: 17 January 2020
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: ship detection; saliency method; superpixel segmentation; kernel density estimation; synthetic aperture radar ship detection; saliency method; superpixel segmentation; kernel density estimation; synthetic aperture radar
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Liang, Y.; Sun, K.; Zeng, Y.; Li, G.; Xing, M. An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images. Remote Sens. 2020, 12, 303.

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