$${\cal L}$$-distribution for multilook polarimetric SAR data and its application in ship detection

Liu, Tao; Tao, Tang; Yang, Ziyuan; Yanni, Jiang; Gao, Sheng; Cheng, Guangquan

doi:10.1007/s11432-019-2779-8

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…And the simulated and real experiments showed that the PDOF detector is effective and robust, and can provide a performance which is similar to those of the best traditional detectors. Most of the polarimetric enhancement methods are often applied to ship detection [18][19][20][21][22]; however, the potential of the polarimetric enhancement method in ship wake detection still needs to be discovered.…”

Section: Introductionmentioning

confidence: 99%

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

Jiang,

Yang,

et al. 2024

Remote Sensing

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Ship wake detection using synthetic aperture radar (SAR) imagery provides a way to obtain small marine ship information, but it often becomes unavailable and unreliable during a high sea state. Polarimetric information provides a potential way to solve this problem, which can enhance the ship target as well as the ship wake features. However, three challenges still exist in ship wake detection in polarimetric SAR imagery: the unwanted influences of bright and singular points on ship wake detection, the lack of performance analysis of wake detection by new-type polarimetric enhancement methods, and the difficulty of using the assessment criteria for ship wake detection. In this paper, we try to solve the above problems. Firstly, fully polarized SAR imagery of both ship turbulent and Kelvin wake is simulated based on the two-scale composite model, and the Polarimetric Whitening Filter (PWF) and Polarimetric Detection Optimization Filter (PDOF) are applied to the simulated fully polarized SAR imagery to enhance the ship wake features. Secondly, since the bright and singular points resulting from the ship echoes and the polarimetric enhancement methods may lead to misdetections, a logarithm process and z-score normalization pre-processing has been applied to the images. Then, a new assessment criterion for wake detection performance has been formulated, and the probability of missing detections (PMDs) and the probability of false alarms (PFAs) have been defined for two different requirements. And a Radon transform-based ship wake detection method for both ship turbulence and Kelvin wake has been carried out in horizontal–horizontal (HH), vertical–vertical (VV), horizontal–vertical (HV), PWF and PDOF SAR imagery. Finally, an analysis of the wake detection performance has been carried out. The PWF and PDOF can improve the wake detection performance by an average of nearly 50 percent compared with the HH and VV.

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Section: Introductionmentioning

confidence: 99%

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

Jiang,

Yang,

et al. 2024

Remote Sensing

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“…Sea ice has a substantial impact on global climate change, geophysical activities such as ocean surface physical properties and currents, and economic and social activities such as maritime shipping and transportation [1]. The freezing and thawing of sea ice and sea ice drift in winter interfere with sea-related engineering, marine trade, and various offshore industrial production activities to varying degrees.…”

Section: Introductionmentioning

confidence: 99%

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Li,

Fan

et al. 2023

Applied Sciences

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Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

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“…After decades of development, fully transparent submersibles have achieved success in the commercial field of sightseeing submersibles, such as the Trion submersible in the United States, C-Quester in the Netherlands, and DW2000 in collaboration with NASA in Canada [6]. Japan has also launched the "Deep Sea 12000" research program, while China put into operation the "Huandao Jiaolong" sightseeing submersible in 2015, making it the world's largest fully transparent passenger-carrying sightseeing submersible [7]. Because of this, the service safety of fully transparent structures is particularly important, whether from a scientific research perspective or with a focus on future commercial value.…”

Section: Introductionmentioning

confidence: 99%

The Ultimate Strength of Fully Transparent Pressure-Resistant Spherical Cabins

Wang,

Li,

et al. 2023

Applied Sciences

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The fully transparent cabin used in a manned submersible is typically made of the viscoelastic material polymethyl methacrylate (PMMA). The pressure-bearing capacity of a PMMA-manned cabin was investigated considering the effects of initial geometrical imperfections and large openings. Three types of cabins were studied within the failure mode of nonlinear buckling, including an intact spherical cabin, a spherical cabin with a single opening, and a spherical cabin with double openings. The initial geometrical imperfection ranges from 0.1% to 0.5% of the inner diameter. The ultimate strength decreasing tendency for the different types of cabins with increasing initial imperfection was obtained and the thickness of the hatch cover determined based on the principle of equivalence differed its effects on the strength of the cabin. The influence of the hatch cover stiffness was not linear and indicated the necessity of exploring the coordinated design between the PMMA shell and the metal hatch cover for the transparent cabin.

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$${\cal L}$$-distribution for multilook polarimetric SAR data and its application in ship detection

Cited by 7 publications

References 32 publications

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

The Ultimate Strength of Fully Transparent Pressure-Resistant Spherical Cabins

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