An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Leng, Xiangguang; Ji, Kefeng; Zhou, Shilin; Xing, Xiangwei; Zou, Huanxin

doi:10.3390/s16091345

Cited by 47 publications

(25 citation statements)

References 38 publications

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“…In other words, region variance, as a basic regional statistical characteristic, can measure the complexity of a given patch to some extent. The similar conclusion could also be found in another model [24]. Therefore, it is reasonable to connect the saliency of a region with its variance.…”

Section: The Proposed Saliency Modelsupporting

confidence: 82%

Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

Dong

Liu

2018

Remote Sensing

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Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts.

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Section: The Proposed Saliency Modelsupporting

confidence: 82%

Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

Dong

Liu

2018

Remote Sensing

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“…Full resolution Level-1 Ground Range Detected (GRD) products with 10 m pixel spacing were obtained. We labeled the location and the box of the ships on SAR images with ship detection software [35] and visual interpretation. Some of them were verified with Automatic Identification System (AIS) information [36].…”

Section: Experiments Dataset and Settingsmentioning

confidence: 99%

Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

et al. 2017

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Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection.

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“…In recent decades, research on Synthetic Aperture Radar (SAR) images [1][2][3] has improved considerably, taking advantage of insensitivity in time and weather. However, low resolution and monochrome SAR images can result in the lack of target texture and color features.…”

Section: Introductionmentioning

confidence: 99%

Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet

Wang

et al. 2018

Remote Sensing

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Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

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An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Cited by 47 publications

References 38 publications

Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet

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