Open AccessA novel Ship detection method from SAR image with reduced false alarm
Author(s) -
J Anil Raj,
Sumam Mary Idicula,
Binu Paul
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1817/1/012010
Subject(s) - artificial intelligence , computer science , deep learning , preprocessor , false alarm , synthetic aperture radar , computer vision , object detection , constant false alarm rate , python (programming language) , pattern recognition (psychology) , image (mathematics) , operating system
Many research works using deep learning techniques for automatic ship detection from SAR images have good detection accuracy. But the main problem in these methods is false detection, mostly due to speckle presence. Therefore, we propose a new deep learning model with a novel preprocessing stage to address this problem. We are introducing a deep learning architecture to detect and localize ships in the SAR image. First, generate a three-channel image from gray-scale SAR image. Then, this image is used to train the model to predict the ship’s position in the SAR image. 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. We used python 3.5 for coding with the Keras framework in the NVIDIA Tesla K80 GPU hardware platform. The experimental results indicated that our proposed method’s ship detection accuracy has increased with reduced false detection percentage.