- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions
摘要： The necessity of efficient monitoring of ships in coastal regions has been increasing over time. Multi-satellite observations make it possible to effectively monitor vessels. This study presents the results of ship detection methodology, applied to optical, hyperspectral, and microwave satellite images in the seas around the Korean Peninsula. Spectral matching algorithms are used to detect ships using hyperspectral images with hundreds of spectral channels and investigate the similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from the backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly in the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.
关键词： ship detection,coastal region,hyperspectral,sustainability,optical remote sensing,SAR
更新于2025-09-23 15:23:52
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Ship detection in spaceborne infrared images based on Convolutional Neural Networks and synthetic targets
摘要： Automatic detection of ships in spaceborne infrared images is important for both military and civil applications due to its all-weather detection capability. While deep learning methods have made great success in many image processing fields recently, training deep learning models still relies on large amount of labeled data, which may limit its application performance for infrared images target detection tasks. Considering that, we propose a new infrared ship detection method based on Convolutional Neural Networks (CNN) which is trained only with synthetic targets. For the problem of limited infrared training data, we design a Transfer Network (T-Net) to generate large amount of synthetic infrared-style ship targets from Google Earth images. The experiments are conducted on a near infrared band image (0:845μm s 0:885μm), a short wavelength infrared band image (1:560μm s 1:66μm) and a long wavelength infrared band image (2:1μm s 2:3μm) of Landsat-8 satellite. The results demonstrate the effectiveness of the target generation ability of T-Net. With only synthetic training samples, our detection method achieves a higher accuracy than other classical ship detection methods.
关键词： Convolutional Neural Networks,Spaceborne infrared image,Synthetic targets,Ship detection
更新于2025-09-23 15:23:52
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Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images
摘要： Ship detection plays a crucial role in remote sensing image processing, which has drawn great attention in recent years. A novel neural network architecture named squeeze excitation skip-connection path networks (SESPNets) is proposed. A bottom-up path is added to feature pyramid network to improve feature extraction capability, and path-level skip-connection structure is firstly proposed to enhance information flow and reduce parameter redundancy. Also, squeeze excitation module is adopted, which can adaptively recalibrate channel-wise feature responses by adding an extra branch after each shortcut path connection block. The multi-scale fused region of interest (ROI) align is then proposed to obtain more accurate and multi-scale proposals. Finally, soft-non-maximum suppression is utilized to overcome the problem of non-maximum suppression (NMS) in ship detection. As demonstrated in the experiments, it can be seen that the SESPNets model has achieved the state-of-the-art performance, which shows the effectiveness of proposed method.
关键词： Skip-connection path networks,Squeeze excitation,Ship detection,Optical remote sensing images,Deep learning
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Performance Analysis of Time-Frequency Technique for the Detection of Small Ships in SAR Imagery at Large Grazing Angle and Moderate Metocean Conditions
摘要： This paper addresses the performance of time-frequency based techniques for the detection of small ships (length less than 30m) under side-looking synthetic aperture radar (SAR) limiting conditions. The aim of this work is to assess this technique for improving TerraSAR-X (TS-X) near real time (NRT) ship detection service. The goals are achieved by processing both the co-polarized single look complex channels (HH and VV) of TS-X data where ships have been identified by their self-reporting messaging system. The results show that the target-to-clutter ratio does not improve significantly for the detection of small ships under the conditions investigated.
关键词： performance analysis,time-frequency methods,ship detection,SAR
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Inshore Ship Detection in Sar Images Based on Deep Neural Networks
摘要： Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.
关键词： object detection networks,full convolutional networks,deep learning,inshore ship detection,Synthetic aperture radar
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Detection Based on Deep Convolutional Neural Networks for Polsar Images
摘要： In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASA/JPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.
关键词： Deep convolutional neural networks,polarimetric synthetic aperture radar (PolSAR),ship detection
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Cnn Based Renormalization Method for Ship Detection in Vhr Remote Sensing Images
摘要： Ship detection with very high resolution (VHR) remote sensing image has recently been an attractive topic due to rapid development of deep learning. Current researches on ship detection are generally confronted with a big challenge that existing methods failed to get high quality of object proposal with good intersection-over-union (IOU) before detection. In this paper, a Convolutional Neural Network (CNN) based renormalization method is proposed to improve the quality of object proposal. First, CNN is used to predict shape information of candidate ships’ which are involved with rotation, location and scale in patches. Then, a renormalization net is designed to adjust the candidate ships in patches by correcting the shape information and renormalizing it to uniform patch. In this way, good candidate objects in patches could be generated and will be helpful with improving following ship detection. The proposed renormalization net was tested on a Google-Earth handcraft dataset. The experimental result demonstrates the proposed renormalization net greatly improve the ship detection with both of good detection accuracy and high IOU.
关键词： Ship detection,CNN,renormalization,remote sensing
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Electromagnetic Modeling of Ships in Maritime Scenarios: Geometrical Optics Approximation
摘要： Global Navigation Satellite System-Reflectometry (GNSS-R), is succesfully employed for ocean altimetric and scatterometric applications. Recently, it has been suggested that GNSS-R can also be used for ship detection applications. To this purpose, an accurate electromagnetic modeling of the bistatic radar cross section of a ship lying over the sea surface would be very helpful. However, existing models are typically limited to monostatic configurations, thus restricting their applicability in multistatic scenarios, such as GNSS-R systems. In this work, we show a procedure to determine the bistatic radar cross section of a ship target, under the geometrical optics approximation. Numerical results show the impact of the geometry of acquisition and polarization on the bistatic radar cross section.
关键词： geometrical optics,radar cross section,Electromagnetic scattering,ship detection
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spaceborne GNSS-Reflectometry for Ship-Detection Applications: Impact of Acquisition Geometry and Polarization
摘要： In this paper, a comparative study of spaceborne Global Navigation Satellite System (GNSS)-Reflectometry for ship detection applications is provided. The analysis is conducted by evaluating the impact of 1) the acquisition geometry and 2) the received signal polarization on ship detectability in GNSS-R data. In particular, the backscattering acquisition geometry is demonstrated to be more suitable for ship detection applications, thus allowing for the detection of 20 m-length ships. Even very large ships are hardly detectable in the conventional forward-scattering geometry. Moreover, receiving right-hand circular polarization is demonstrated to provide significant improvements of the signal-to-noise-plus-clutter with respect to the conventional left-hand circular polarization channel, conventionally exploited in GNSS-R remote sensing. The study is based on a numerical tool for the bistatic radar cross section of the ship, which is presented in a companion paper.
关键词： backscattering geometry,GNSS-Reflectometry,bistatic radar,ship detection,maritime surveillance
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Rotated Region Based Fully Convolutional Network for Ship Detection
摘要： Ship detection from high-resolution optical remote sensing images has been a prevalent domain in recent years. Unlike objects in natural images, ships of interest can be anywhere in optical remote sensing images with multi-scale and multi-oriented which makes it more difficult to be detected. In this paper, we propose a novel method based on the fully convolutional network to detect ships. Our method has three important components: 1) we design a network merging different levels of feature map to fuse multi-scale information. Determining the existence of large ship require features from deep layers in the network, while predicting rotated bounding box enclosing small ships needs shallow layers information; 2) The network can be trained end-to-end to generate score maps which indicates the confidence score for the ship region of interest in pixel-wise level through all locations and scale of an image; 3) We design a rotated bounding box regression model to localize the ships. The experimental results on our dataset collected from Google Earth has demonstrated our proposed method achieves promising performance on ship detection in terms of both efficiency and accuracy in high-resolution optical remote sensing images.
关键词： Ship detection,Rotated region,Fully Convolutional network
更新于2025-09-23 15:21:21