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 | |
刊名 | IEEE Transactions on Geoscience and Remote Sensing |
2022 | |
卷号 | 60页码:1-9 |
关键词 | Brain-inspired deep neural networks (DNNs) Fast Saliency-based Filtering algorithm (FSF) filtering mechanisms priority map saliency map |
ISSN号 | 0196-2892 |
产权排序 | 1 |
英文摘要 | 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. |
语种 | 英语 |
WOS记录号 | WOS:000726094900101 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/28329] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Luo HB(罗海波) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China, 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China 4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China 5.University of Chinese Academy of Sciences, Beijing 100049, China |
推荐引用方式 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,2022,60:1-9. |
APA | Zhang PP,Luo HB,Ju MR,He M,Chang Z,&Hui B.(2022).Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images.IEEE Transactions on Geoscience and Remote Sensing,60,1-9. |
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 60(2022):1-9. |
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