Abstract
Sea-surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multiframe detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential range–Doppler (RD) spectra. A two-step detection framework is proposed, where the intraframe and interframe detections are achieved using the differences in features and interframe correlations between the moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multiframe detection algorithms. Additionally, this approach exhibits acceptable generalization ability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Geoscience and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
