A novel adaptive synthetic aperture radar ship detection system

Stastny, John; Hughes, Michael; Garcia, Dan; Bagnall, Bryan; Pifko, Keith; Buck, Heidi; Sharghi, Elan

doi:10.23919/oceans.2011.6106905

Cited by 15 publications

(9 citation statements)

References 11 publications

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“…Spaceborne SAR is an applied formation of SAR in space. Spaceborne SAR has some characteristic differences compared to airborne SAR [28][29][30][31], e.g., the former image normally has large data size due to its large antenna beam irradiation range, etc.…”

Section: Ghost Phenomenon In Spaceborne Sarmentioning

confidence: 99%

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

et al. 2019

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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.Remote Sens. 2019, 11, 620 2 of 17 recognition [11]. In addition, the conventional constant false alarm rate (CFAR) technique is a typical detection method based on the grayscale feature. However, complicated and cluttered backgrounds severely affect CFAR detection performance [12].In recent years, ship target detection based on deep learning (DL) has been widely studied [13,14], using the typical model of convolutional neural network (CNN) [15]. Liu et al. [16] presented a ship detection method, namely sea-land segmentation-based convolutional neural network (SLS-CNN), which combines a SLS-CNN detector, saliency computation, and corner features. Furthermore, Zhao et al. [17] proposed a spaceborne SAR ship detection algorithm based on low complexity CNN. Some other well-known CNN-based target detection methods include faster region-CNN (Faster R-CNN), you only look once (YOLO) list model, etc. For example, Li et al. [18,19] improved detection performance using Faster R-CNN, to successfully provide a densely connected multi-scale neural network [19]. This method is used to solve multi-scale and multi-scene problems in SAR ship detection. Feature maps are fused by densely connecting different feature map layers, rather than information from single feature maps, which represent top-to-down feature map connections. The R-CNN method is used for target recognition in large scene SAR images [20]. Furthermore, Hamza and Cai used YOLOv2 for ship detection [21], which introduced a multitude of enhancements into the original YOLO model.However, these methods may be no longer effective when ghost replicas exist in an imaged scene. The ghost phenomenon is an intrinsic effect of SAR's ambiguity, both in azimuth and range [22,23]. Range ambiguity occurs when different backscattered echoes-one related to a transmitted pulse and the other due to a previous transmission-temporarily overlap during the receiving operation [24]. On the other hand, azimuth ambiguity is caused by the aliasing of each target's Doppler phase...

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Section: Ghost Phenomenon In Spaceborne Sarmentioning

confidence: 99%

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

et al. 2019

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“…Stastny et al 28 develop a SAR ship detection system and present a series of methods to remove false alarms resulting from the above causes. The primary false alarms contain smearing due to the movement of objects within the radar area during data acquisition, ghost ships resulting from azimuth ambiguities, and multiple detections of the same object due to sidelobes.…”

Section: Discriminationmentioning

confidence: 99%

“…We reimplement the method of Margarit and Tabasco 8 by training reference ships with real SAR data. Lower recognition accuracy for container ships (50.00%) is mainly due to inadequate training samples (28), which can be improved by further expanding the database in future work. Because the proposed method is a supervised classification algorithm, we split the data into training set and testing set.…”

Section: Recognition Performance Evaluationmentioning

confidence: 99%

Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery

Lang

Zhang

et al. 2014

J. Appl. Remote Sens

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Ship surveillance by remote sensing technology has become a valuable tool for protecting marine environments. In recent years, the successful launch of advanced synthetic aperture radar (SAR) sensors that have high resolution and multipolarimetric modes has enabled researchers to use SAR imagery for not only ship detection but also ship category recognition. A hierarchical ship detection and recognition scheme is proposed. The complementary information obtained from multipolarimetric modes is used to improve both the detection precision and the recognition accuracy. In the ship detection stage, a three-class fuzzy c-means clustering algorithm is used to calculate the segmenting threshold for prescreening ship candidates. To reduce the false alarm rate (FAR), we use a two-step discrimination strategy. In the first step, we fuse the detection results from multipolarimetric channels to reduce the speckle noise, ambiguities, sidelobes, and other sources of interference. In the second step, we use a binary classifier, which is trained with prior data collected on ships and nonships, to reduce the FAR even further. In the ship category recognition stage, we concatenate texturebased descriptors extracted from multiple polarmetric channels to construct a robust ship representation for category recognition. Furthermore, we construct and release a ship category database with real SAR data. We hope that it can be used to promote investigations of SAR ship recognition in the remote sensing and related academic communities. The proposed method is validated by a comprehensive experimental comparison to the state-of-the-art methods. The validation procedure showed that the proposed method outperforms all of the competing methods by about 5% and 15% in terms of ship detection and recognition, respectively.

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“…[1][2][3] The FMV capability focuses on automatically detecting and tracking maritime targets (against a maritime background) in full-motion video; it is intended to be a video analysis tool for extracting meaningful data from FMV with minimal human supervision.…”

Section: Introductionmentioning

confidence: 99%

Marine object detection in UAV full-motion video

Parameswaran¹,

Lane²,

Bagnall³

et al. 2014

SPIE Proceedings

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Recent years have seen an increased use of Unmanned Aerial Vehicles (UAV) with video-recording capability for Maritime Domain Awareness (MDA) and other surveillance operations. In order for these efforts to be effective, there is a need to develop automated algorithms to process the full-motion videos (FMV) captured by UAVs in an efficient and timely manner to extract meaningful information that can assist human analysts and decision makers. This paper presents a generalizeable marine object detection system that is specifically designed to process raw video footage streaming from UAVs in real-time. Our approach does not make any assumptions about the object and/or background characteristics because, in the MDA domain, we encounter varying background and foreground characteristics such as boats, bouys and ships of varying sizes and shapes, wakes, white caps on water, glint from the sun, to name but a few. Our efforts rely on basic signal processing and machine learning approaches to develop a generic object detection system that maintains a high level of performance without making prior assumptions about foreground-background characteristics and does not experience abrupt performance degradation when subjected to variations in lighting, background characteristics, video quality, abrupt changes in video perspective, size, appearance and number of the targets. In the following report, in addition to our marine object detection system, we present representative object detection results on some real-world UAV full-motion video data.

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A novel adaptive synthetic aperture radar ship detection system

Cited by 15 publications

References 11 publications

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

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

Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery

Marine object detection in UAV full-motion video

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