The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.

https://www.mdpi.com/2072-4292/11/6/620/pdf

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

Land contained sea area ship detection using spaceborne image

In view of synthetic aperture radar (SAR) target detection, traditional methods are based on hand-crafted feature extraction and classifier. Besides, deep learning (DL) based methods are research hotspots in recent year. However, their shortcomings cannot be neglected, i.e. detection accuracy of traditional method needs to be improved and DL features are difficult to interpret. To overcome these problems, a target detection method with multi-features in SAR imagery is proposed in this paper. It consists of two parallel sub-channels. DL features and hand-crafted features are extracted in these channels, respectively. Here, convolutional neural network (CNN) model is applied to capture DL features of original SAR images. Deep neural network (NN) is used to further analyze hand-crafted features. Furthermore, two sub-channel features are concatenated together in the main channel. After several layers network processing, fused deep features are extracted. Finally, softmax classifier is applied to discriminate ship target. According to the experiments based on Sentinel-1 SAR data, we can find that the detection performance is improved by the proposed method.

Deep learning based target detection method with multi-features in SAR imagery

Using synthetic aperture radar (SAR) for ship detection is a significant application for maritime monitoring and security. Ship detectors depress the disturbance from the sea surface such that the background consistently shows the sea pixels. However, in addition to water, the vast sea also contains other real objects, of which small islands are the most common. In this paper, we investigated the detection of ships in a sea area containing small islands. Based on the most widely used ship detector-constant false alarm rate (CFAR), three aspects were selected to investigate the island appearance effect, namely, image resolution, polarization, and incidence angle. As the result, an output strategy is provided when an island exists, and a ship-island enhance operator is proposed.

SAR-based ship detection in sea areas containing small islands

Ship detection is a significant application of maritime monitoring and security To fully explore the potential of wide
coverage of synthetic aperture radar (SAR) image, the ScanSAR Wide image for ship detection is investigated in this
paper The Radarsat-2 ScanSAR Wide mode image is used as the image source due to its huge coverage and constant
false alarm rate (CFAR) with Gamma distribution is selected as the core detector Two problems of ScanSAR ship
detection, the unbalanced phenomenon and false alarms of islands, are investigated and solved by a compensation step
and Hessian matrix respectively For more aspects, the detector also concerns the polarization channel selection and
distribution fitting Finally, a whole flow chart of ScanSAR ship detection is presented As test cases, the experimental
image is used to show the efficiency of our method

Ship surveillance with Radarsat-2 ScanSAR

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Ship surveillance with Radarsat-2 ScanSAR