首页|Density-based ship detection in SAR images:Extension to a self-similarity perspective

Density-based ship detection in SAR images:Extension to a self-similarity perspective

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Nonlocal self-similarity is an important property of Synthetic Aperture Radar(SAR)images to characterize the repetitiveness of features embodied by SAR images within nonlocal areas and has been used for enhancement of SAR images.Existing SAR ship detectors often indepen-dently handle small sub-images cropped from a large marine SAR image and do not exploit the nonlocal self-similarity therein.In this paper,we propose a new ship detector from the perspective of nonlocal self-similarity in SAR images to improve the ship detection performance,basically including three stages:prescreening,intra-cue calculation,and inter-cue calculation.In the pre-screening stage,we design a new Histogram-based Density(HD)feature to rapidly select candidate sub-images potentially containing ship targets from a large SAR image.In the intra-cue calculation stage,target cues within a single candidate sub-image are extracted.In the inter-cue calculation stage,thanks to the nonlocal self-similarity among different candidate sub-images in terms of den-sity features,we innovatively extract a weighted superpixel-HD map to obtain accumulated intra-cues across all the candidate sub-images.Finally,for each candidate sub-image,we fuse its inter-cue and intra-cue to obtain final detection results.Experimental results based on real SAR images show that our newly proposed method provides a better target-to-clutter contrast and ship detection per-formance than those of other state-of-the-art detection approaches.

Ship detectionSynthetic aperture radar(SAR)DensitySelf-similarityHistogram

Xueqian WANG、Gang LI、Zhizhuo JIANG、Yu LIU、You HE

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Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

Shenzhen International Graduate School,Tsinghua University,Shenzhen 518055,China

国家重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金Autonomous Research Project of Department of Electronic Engineering at Tsinghua University

2021YFA0715201619251066202209262101303

2024

10.1016/j.cja.2023.11.017

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(3)
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