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Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images
Zhang PP(张盼盼)1,2,3,4,5; Luo HB(罗海波)1,2,3,4,5; Ju MR(鞠默然)1,2,3,4,5; He M(何淼)1,2,3,4,5; Chang Z(常铮)1,2,3,4,5; Hui B(惠斌)1,2,3,4,5
Department光电信息技术研究室
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
2021
Pages1-9
Indexed ByEI
EI Accession number20210809949130
Contribution Rank1
KeywordBrain-inspired deep neural networks (DNNs) Fast Saliency-based Filtering algorithm (FSF) filtering mechanisms priority map saliency map
Abstract

In this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recent years, deep neural networks (DNNs) have made substantial improvements on detection by adopting data augmentation. However, the improvement is limited since the models are sensitive to noise. To address this problem, a Fast Saliency-based Filtering algorithm (FSF) is proposed to filter out interference information. The FSF method is inspired by the filtering mechanisms of the human brain, which help people filter out target-irrelevant information fast to better extract target-relevant information. The FSF includes two parts of the bottom-up process and the top-down process. The bottom-up process is used to extract a saliency map of an input image, and the other one is used to filter out target-irrelevant information based on the saliency map. The FSF can be a front-end preprocessing module of DNNs to fast filter out target-irrelevant information and obtain a primary priority map of an input image. Experimental results demonstrate that our brain-inspired FSF method obtains obvious improvement of detection performance on AIR-SARShip-1.0.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28329
Collection光电信息技术研究室
Corresponding AuthorLuo HB(罗海波)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China,
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
5.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China
Recommended Citation
GB/T 7714
Zhang PP,Luo HB,Ju MR,et al. Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing,2021:1-9.
APA Zhang PP,Luo HB,Ju MR,He M,Chang Z,&Hui B.(2021).Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images.IEEE Transactions on Geoscience and Remote Sensing,1-9.
MLA Zhang PP,et al."Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images".IEEE Transactions on Geoscience and Remote Sensing (2021):1-9.
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