A novel sarnede method for real-time ship detection from synthetic aperture radar image

• Published in: Multimedia tools and applications Vol. 81; no. 12; pp. 16921 - 16944
• Main Authors: J, Anil Raj, Idicula, Sumam Mary, Paul, Binu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2022; Springer Nature B.V
• Subjects: Accuracy; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Datasets; Deep learning; Image processing; Image quality; Machine learning; Multimedia Information Systems; Object recognition; Pattern recognition; Preprocessing; Radar detection; Radar imaging; Real time; Remote sensing; Ships; Special Purpose and Application-Based Systems; Synthetic aperture radar; Deep learning; Ship detection; SAR; Convolution neural network; Marine target detection
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12243-1

Deep learning-based ship detection from SAR data is one of the challenging problems in the remote sensing area. Also, SAR ship detection is precise object detection and pattern recognition task under the computer vision area. The main problems are false detection, primarily due to speckle presence and multi-scale SAR image availability. We propose a novel real-time system with a preprocessing technique exclusively for SAR ship detection to address this problem. The proposed SarNeDe preprocessing stage is specially designed using image processing techniques and lee filter to reduce the false prediction and improve the SAR image quality, which increases the detection accuracy because the lee filter alone could increase missed detections. The SarNeDe image is generated from raw SAR image and is given to a novel multi-scale lightweight deep learning model to predict all ships’ positions. The proposed model has a feature merging & boosting network and three detection parts for detecting big, medium, & small ships. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method’s feasibility. The experimental results indicated that our proposed method’s ship detection accuracy is superior to other state-of-the-art ship detectors with reduced false detections.