Abstract
Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.
Highlights
With continuous development of Synthetic Aperture Radar (SAR) technology, an increasing number of very high resolution (VHR) SAR images have been obtained, providing a new way to strengthen marine monitoring
In order to solve the existing problems, this paper proposes a whole workflow consisting of sea-land segmentation, cropping with overlapping, detection with pre-trained MR-SSD, coordinates mapping and predicted boxes consolidation for marine target detection in large-scale SAR images
Each row in the table denotes the actual target class, while each column represents the class predicted by MT-convolutional neutral network (CNN)
Summary
With continuous development of Synthetic Aperture Radar (SAR) technology, an increasing number of very high resolution (VHR) SAR images have been obtained, providing a new way to strengthen marine monitoring. Different from optical sensors, SAR is capable of working in all-day and all-weather conditions, and it is receiving more and more attention It is very time-consuming to interpret SAR images manually because of speckle noise, false targets, etc. The work in Reference [5] employed histogram of oriented gradients (HOG) features and dictionary learning to performing classification with an accuracy of 97.5% for the three kinds of ships. While these feature-based classifiers can achieve high performance, the features have to be carefully designed especially when dealing with a wide variety of targets. The classifier-combination strategy increases the computational complexity as it applies the classifiers one by one
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