Year: 2019
Type: article
Abstract: Synthetic Aperture Radar (SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process. In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio (SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
Source: Chinese Journal of Aeronautics
Authors: Fei GAO, Aidong LIU, Kai LIU, Erfu YANG, Amir HUSSAIN
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,000
Awards: G2059941093, G2124738211, 分布式雷达空间目标三维成像方法研究, G2338651751, G2396582510, G2565164337, G3025588869, G3320033847, G3835062404, G3959312736, G4072948799, Towards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devices (AV-COGHEAR), G4749791213, G5186289593, 生物启发式的雷达图像半监督识别关键技术研究, G6645057603, G7488069868, G7851277957, G8088923933, 新手性催化剂催化下手性加成反应的研究, 机载监视雷达地面运动目标跟踪技术研究, G8932669053, Dual Process Control Models in the Brain and Machines with Application to Autonomous Vehicle Control
OpenAlex ID: W2941889397