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Готові списки джерел за темами / Sea clutter extraction / Статті в журналах

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Статті в журналах з теми "Sea clutter extraction"

Автор: Grafiati

Опубліковано: 16 вересня 2023

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1

Zhang, Le, Anke Xue, Xiaodong Zhao, Shuwen Xu, and Kecheng Mao. "Sea-Land Clutter Classification Based on Graph Spectrum Features." Remote Sensing 13, no. 22 (2021): 4588. http://dx.doi.org/10.3390/rs13224588.

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In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of th

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2

Zhang, Ling, Wei You, Q. Wu, Shengbo Qi, and Yonggang Ji. "Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR." Remote Sensing 10, no. 10 (2018): 1517. http://dx.doi.org/10.3390/rs10101517.

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High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of

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3

Zhao, Di, Hongyan Xing, Haifeng Wang, Huaizhou Zhang, Xinyi Liang, and Haoqi Li. "Sea-Surface Small Target Detection Based on Four Features Extracted by FAST Algorithm." Journal of Marine Science and Engineering 11, no. 2 (2023): 339. http://dx.doi.org/10.3390/jmse11020339.

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On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization.

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4

Duan, Guoxing, Yunhua Wang, Yanmin Zhang, Shuya Wu, and Letian Lv. "A Network Model for Detecting Marine Floating Weak Targets Based on Multimodal Data Fusion of Radar Echoes." Sensors 22, no. 23 (2022): 9163. http://dx.doi.org/10.3390/s22239163.

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Due to the interaction between floating weak targets and sea clutter in complex marine environments, it is necessary to distinguish targets and sea clutter from different dimensions by designing universal deep learning models. Therefore, in this paper, we introduce the concept of multimodal data fusion from the field of artificial intelligence (AI) to the marine target detection task. Using deep learning methods, a target detection network model based on the multimodal data fusion of radar echoes is proposed. In the paper, according to the characteristics of different modalities data, the temp

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5

Jiang, Yingqi, Lili Dong, and Junke Liang. "Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination." Sensors 22, no. 15 (2022): 5873. http://dx.doi.org/10.3390/s22155873.

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Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship

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6

Pan, Xueli, Nana Li, Lixia Yang, et al. "Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images." Remote Sensing 15, no. 13 (2023): 3258. http://dx.doi.org/10.3390/rs15133258.

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Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutt

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7

Farshchian, Masoud. "Target Extraction and Imaging of Maritime Targets in the Sea Clutter Spectrum Using Sparse Separation." IEEE Geoscience and Remote Sensing Letters 14, no. 2 (2017): 232–36. http://dx.doi.org/10.1109/lgrs.2016.2636253.

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8

Ningbo, Liu, Xu Yanan, Ding Hao, Xue Yonghua, and Guan Jian. "High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network." Computer Communications 147 (November 2019): 76–84. http://dx.doi.org/10.1016/j.comcom.2019.08.016.

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9

Wu, Zheng Long, Jie Li, and Zhen Yu Guan. "Feature Extraction of Underwater Target Ultrasonic Echo Based on Wavelet Transform." Applied Mechanics and Materials 599-601 (August 2014): 1517–22. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1517.

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Ultrasonic detection has been widely used in underwater detectoscopes as an important method for underwater detection. Feature extraction of echo signal time-delay and amplitude is the main task of processing underwater ultrasonic signal. Underwater target ultrasonic echo signal is influenced by reverberation and noise from the sea and system itself, reverberation interference of signal background is the main difficulty for target echo detection. So we use denoising algorithm to denoise echo signal. At first this paper denoises the measured weighted background clutter data using wavelet thresh

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10

Chen, Xiaolong, Jian Guan, Zhonghua Bao, and You He. "Detection and Extraction of Target With Micromotion in Spiky Sea Clutter Via Short-Time Fractional Fourier Transform." IEEE Transactions on Geoscience and Remote Sensing 52, no. 2 (2014): 1002–18. http://dx.doi.org/10.1109/tgrs.2013.2246574.

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11

Luo, Zhongtao, Taifeng Wu, Zishu He, and Xuyuan Chen. "Extraction of sea‐clutter and RFI regions based on image segmentation for high‐frequency sky‐wave radar." IET Radar, Sonar & Navigation 13, no. 1 (2019): 58–64. http://dx.doi.org/10.1049/iet-rsn.2018.5128.

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12

Chen, Xiaolong, Jian Guan, Xiaoqian Mu, Zhigao Wang, Ningbo Liu, and Guoqing Wang. "Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet." Remote Sensing 13, no. 19 (2021): 3856. http://dx.doi.org/10.3390/rs13193856.

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Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The pred

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13

Hu, Jianming, Xiyang Zhi, Wei Zhang, Longfei Ren, and Lorenzo Bruzzone. "Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images." Remote Sensing 12, no. 20 (2020): 3370. http://dx.doi.org/10.3390/rs12203370.

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Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the backg

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14

Gao, Fei, Wei Shi, Jun Wang, Erfu Yang, and Huiyu Zhou. "Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images." Remote Sensing 11, no. 22 (2019): 2694. http://dx.doi.org/10.3390/rs11222694.

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Independent of daylight and weather conditions, synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods for SAR ship detection are highly dependent on the statistical models of sea clutter or some predefined thresholds, and generally require a multi-step operation, which results in time-consuming and less robust ship detection. Recently, deep learning algorithms have found wide applications in ship detection from SAR images. However, due to the multi-resolution imaging mode and complex background, it is hard for the network to extract representa

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15

Li, B., B. Xu, and Y. Yuan. "Extraction of mixed-order multicomponent ship target signals from broadened sea clutter in bistatic shipborne surface wave radar." IET Radar, Sonar & Navigation 3, no. 3 (2009): 214. http://dx.doi.org/10.1049/iet-rsn:20070138.

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16

Joshi, Sushil Kumar, Stefan V. Baumgartner, Andre B. C. da Silva, and Gerhard Krieger. "Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection." Remote Sensing 11, no. 11 (2019): 1270. http://dx.doi.org/10.3390/rs11111270.

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Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppl

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17

Kubicek, Bernice, Ananya Sen Gupta, and Ivars Kirsteins. "Statistical-based feature extraction and classification of active sonar data." Journal of the Acoustical Society of America 151, no. 4 (2022): A267—A268. http://dx.doi.org/10.1121/10.0011297.

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Sonar target recognition is difficult due to the potential nonlinear overlap within an acoustic color response due to various backscatter and clutter within the ocean. This talk presents initial results from using a statistical model of feature vectors in conjunction with machine learning classifiers. Canonical correlation analysis (CCA) seeks to find two linear combinations of data by maximizing the correlation between the linear combinations while maintaining unit variance. In this application, CCA is used as a feature extraction method before target classification of active sonar data exper

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18

Lee, Seungwoo, Iksu Seo, Jongwon Seok, Yunsu Kim, and Dong Seog Han. "Active Sonar Target Classification with Power-Normalized Cepstral Coefficients and Convolutional Neural Network." Applied Sciences 10, no. 23 (2020): 8450. http://dx.doi.org/10.3390/app10238450.

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Анотація:

Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for va

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19

Pang, Lei, Baoxuan Li, Fengli Zhang, Xichen Meng, and Lu Zhang. "A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection." Sensors 22, no. 18 (2022): 7088. http://dx.doi.org/10.3390/s22187088.

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Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and the background is complicated due to the influence of sea clutter and proximity to land, leading to the accuracy problem of ship monitoring. Compared with traditional methods, deep learning has powerful data processing ability and feature extraction ability, but its complex model and calculations lead to a certain degree of d

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20

Hu, Jianming, Xiyang Zhi, Tianjun Shi, Lijian Yu, and Wei Zhang. "Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation." Remote Sensing 13, no. 23 (2021): 4840. http://dx.doi.org/10.3390/rs13234840.

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Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. Moreover, most of them ignore the effective retention of position information in the feature extraction process, which reduces the contribution of features to subsequent classification. To overcome these limitations, we propose a novel ship detection framework combining the dilated rate selection and attention-guided

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21

Li, Guannan, Ying Li, Yongchao Hou, Xiang Wang, and Lin Wang. "Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data." Remote Sensing 13, no. 9 (2021): 1607. http://dx.doi.org/10.3390/rs13091607.

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Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set

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22

Yang, Xuguang, Changjun Yu, Aijun Liu, Linwei Wang, and Taifan Quan. "The Vertical Ionosphere Parameters Inversion for High Frequency Surface Wave Radar." International Journal of Antennas and Propagation 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/8609372.

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High Frequency Surface Wave Radar (HFSWR), which is currently applied in over-the-horizon detection of targets and sea states remote sensing, can receive a huge mass of ionospheric echoes, making it possible for the ionospheric clutter suppression to become a hot spot in research area. In this paper, from another perspective, we take the ionospheric echoes as the signal source rather than clutters, which provides the possibility of extracting information regarding the ionosphere region and explores a new application field for HFSWR. Primarily, pretreatment of threshold segmentation as well as

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23

Huo, Weibo, Jifang Pei, Yulin Huang, Qian Zhang, and Jianyu Yang. "A New Maritime Moving Target Detection and Tracking Method for Airborne Forward-looking Scanning Radar." Sensors 19, no. 7 (2019): 1586. http://dx.doi.org/10.3390/s19071586.

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Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spec

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24

Yang, Tian-Ci, Ye Zhao, Guo-Shan Wu, and Xin-Cheng Ren. "Study on Doppler Spectra of Electromagnetic Scattering of Time-Varying Kelvin Wake on Sea Surface." Sensors 22, no. 19 (2022): 7564. http://dx.doi.org/10.3390/s22197564.

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In general, it is more practical to detect ship wake under the background of a complicated sea state than the ship directly. Thus, in this paper, the Doppler spectra of time-varying Kelvin wake on a time-varying sea surface are numerically investigated by considering the change of ship wake with time in ocean environments. For this purpose, the linear superposition model of a time-varying sea surface and a time-varying Kelvin wake is established. Combined with the facet scattering field model of sea surface and Kirchhoff approximation (KA), the Doppler of the radar scattering echo signal of th

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25

Yang, Zhiqing, Jianjiang Tang, Hao Zhou, Xinjun Xu, Yingwei Tian, and Biyang Wen. "Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar." Remote Sensing 13, no. 8 (2021): 1548. http://dx.doi.org/10.3390/rs13081548.

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Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection algorithm based on time-frequency (TF) analysis and the CFAR method is proposed in this paper. After the TF ridge extraction, CFAR detection is performed to test each sample of the ridges, and a binary integration is run to determine whether the entire TF ridge is of a ship. To verify the effectiveness of the

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26

Sun, Lei, Zhizhong Lu, Hui Wang, Hong Liu, and Xiuneng Shang. "A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar." Journal of Sensors 2022 (February 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/1068885.

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To suppress the influence of rainfall when extracting sea surface wind and wave parameters using X-band marine radar and control the quality of the collected radar image, it is necessary to detect whether the radar image is contaminated by rainfall. Since the detection accuracy of the statistical characteristics methods (e.g., the zero pixel percentage method and the high-clutter direction method) is limited and the threshold is difficult to determine, the machine learning methods (e.g., the support vector machine-based method and the neural network algorithm) are difficult to select appropria

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27

Su, Liyun, and Xiu Ling. "Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network." Complexity 2020 (July 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/3284587.

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In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presente

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28

Chen, Zhe, Zhiquan Ding, Xiaoling Zhang, Xiaoting Wang, and Yuanyuan Zhou. "Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images." Remote Sensing 15, no. 15 (2023): 3868. http://dx.doi.org/10.3390/rs15153868.

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Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are establishe

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29

Shao, Zhiyu, Jiangheng He, and Shunshan Feng. "Extraction of a target in sea clutter via signal decomposition." Science China Information Sciences 63, no. 2 (2019). http://dx.doi.org/10.1007/s11432-018-9859-4.

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30

Bi, Xiaowen, Shenglong Guo, Yunxiu Yang, and Qin Shu. "Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering." IEEE Transactions on Geoscience and Remote Sensing, 2022, 1. http://dx.doi.org/10.1109/tgrs.2022.3192893.

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31

Wu, Xijie, Hao Ding, Ningbo Liu, Yunlong Dong, and Jian Guan. "Priori Information Based Feature Extraction Method for Small Target Detection in Sea Clutter." IEEE Transactions on Geoscience and Remote Sensing, 2022, 1. http://dx.doi.org/10.1109/tgrs.2022.3188046.

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32

Li, Qing. "Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering." Frontiers in Marine Science 9 (October 19, 2022). http://dx.doi.org/10.3389/fmars.2022.1008242.

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Electromagnetic field noise and clutter generated from the motion of ocean waves are the main obstacles in the research of magnetotelluric dynamic analysis, and it is difficult to extract the crossed instantaneous frequencies (IFs) of underwater electromagnetic detected (UEMD) data due to the limited resolution of the current time-frequency techniques. To alleviate this bottleneck issue, a new spatio-temporal nonconvex penalty adaptive chirp mode decomposition (STNP-ACMD) is originally proposed for separating each mono-component individually from a complicated multi-component with severely cro

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