A ship detection system for RADARSAT-2 dual-pol multi-look imagery implemented in the ADSS

Crisp, David J.

doi:10.1109/radar.2013.6652006

Cited by 37 publications

(24 citation statements)

References 9 publications

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“…(2) To improve the relevance of contextual information, we propose a context information scene perception (CISP) module to obtain the context information for targets of different scales. (3) We show that CISPNet can implement relatively advanced and reliable performance on remote sensing image dataset and the NWPU VRH-10 dataset and verify the effectiveness of the optimization for its architecture.…”

Section: Introductionmentioning

confidence: 65%

“…With the rapid development of remote sensing spaceborne technologies, such as Sentinel-1, TerraSAR-X, and RADARSAT-2 [1][2][3], target detection on remote sensing images has been playing an important position in the field of civil areas and defense security [4][5][6]. However, target detection in remote sensing images remains a great challenge due to the complex background and existence of speckle noises in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

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CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

et al. 2019

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The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 × 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.

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Section: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

et al. 2019

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“…2018, 10, 132 3 of 14 1. Different from previous detection models, we build a new ship detection framework based on rotation regions which can handle different complex scenes, detect intensive objects, and reduce redundant detection regions.…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

et al. 2018

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Ship detection has been playing a significant role in the field of remote sensing for a long time, but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection, and the redundancy of the detection region. In order to solve these problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving problems resulting from the narrow width of the ship. Compared with previous multiscale detectors such as Feature Pyramid Network (FPN), DFPN builds high-level semantic feature-maps for all scales by means of dense connections, through which feature propagation is enhanced and feature reuse is encouraged. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has state-of-the-art performance.

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“…Since the launch of the Seasat in 1978, the number of spaceborne SAR sensors has drastically increased [2,3]. It is a mature and successful discipline for global ocean monitoring [3][4][5][6]. Advanced spaceborne SAR sensors today provide fine resolutions, such as TerraSAR-X, COSMOS-SkyMed, RADARSAT-2, GF-3, and Sentinel-1 [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…It is a mature and successful discipline for global ocean monitoring [3][4][5][6]. Advanced spaceborne SAR sensors today provide fine resolutions, such as TerraSAR-X, COSMOS-SkyMed, RADARSAT-2, GF-3, and Sentinel-1 [4][5][6][7][8][9]. As more than 90% of the world's trade goods, and more than 70% of global crude oil are transported by sea, maritime surveillance is of utmost importance [10].…”

Section: Introductionmentioning

confidence: 99%

Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

Leng

Zhou

et al. 2017

Remote Sensing

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Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based sources, whereas they are usually regions of interest (ROI). Given the presence of complexity and diversity in littoral zones, azimuth ambiguities removal is a tough problem. As SAR sensors can have a repeat cycle, multi-temporal SAR images provide new insight into this problem. A method for azimuth ambiguities removal in littoral zones based on multi-temporal SAR images is proposed in this paper. The proposed processing chain includes co-registration, local correlation, binarization, masking, and restoration steps. It is designed to remove azimuth ambiguities caused by fixed land-based sources. The idea underlying the proposed method is that sea surface is dynamic, whereas azimuth ambiguities caused by land-based sources are constant. Thus, the temporal consistence of azimuth ambiguities is higher than sea clutter. It opens up the possibilities to use multi-temporal SAR data to remove azimuth ambiguities. The design of the method and the experimental procedure are based on images from the Sentinel data hub of Europe Space Agency (ESA). Both Interferometric Wide Swath (IW) and Stripmap (SM) mode images are taken into account to validate the proposed method. This paper also presents two RGB composition methods for better azimuth ambiguities visualization. Experimental results show that the proposed method can remove azimuth ambiguities in littoral zones effectively.

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A ship detection system for RADARSAT-2 dual-pol multi-look imagery implemented in the ADSS

Cited by 37 publications

References 9 publications

CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

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