Готові списки джерел за темами / Sea clutter extraction

Добірка наукової літератури з теми "Sea clutter extraction"

Автор: Grafiati
Опубліковано: 16 вересня 2023

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Статті в журналах з теми "Sea clutter extraction"

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 (November 15, 2021): 4588. http://dx.doi.org/10.3390/rs13224588.

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Анотація:
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 the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
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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 (September 21, 2018): 1517. http://dx.doi.org/10.3390/rs10101517.

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Анотація:
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 HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.
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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 (February 3, 2023): 339. http://dx.doi.org/10.3390/jmse11020339.

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Анотація:
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. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.
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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 (November 25, 2022): 9163. http://dx.doi.org/10.3390/s22239163.

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Анотація:
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 temporal LeNet (T-LeNet) network module and time-frequency feature extraction network module are constructed to extract the time domain features, frequency domain features, and time-frequency features from radar sea surface echo signals. To avoid the impact of redundant features between different modalities data on detection performance, a Self-Attention mechanism is introduced to fuse and optimize the features of different dimensions. The experimental results based on the publicly available IPIX radar and CSIR datasets show that the multimodal data fusion of radar echoes can effectively improve the detection performance of marine floating weak targets. The proposed model has a target detection probability of 0.97 when the false alarm probability is 10−3 under the lower signal-to-clutter ratio (SCR) sea state. Compared with the feature-based detector and the detection model based on single-modality data, the new model proposed by us has stronger detection performance and universality under various marine detection environments. Moreover, the transfer learning method is used to train the new model in this paper, which effectively reduces the model training time. This provides the possibility of applying deep learning methods to real-time target detection at sea.
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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 (August 5, 2022): 5873. http://dx.doi.org/10.3390/s22155873.

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Анотація:
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 between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.
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6

Pan, Xueli, Nana Li, Lixia Yang, Zhixiang Huang, Jie Chen, Zhenhua Wu, and Guoqing Zheng. "Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images." Remote Sensing 15, no. 13 (June 24, 2023): 3258. http://dx.doi.org/10.3390/rs15133258.

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Анотація:
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 clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.
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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 (February 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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Анотація:
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 threshold denoising method, then the paper extracts breaking points of echo signal through wavelet transform, at last the paper makes an envelope extraction using Hilbert transform combined with wavelet transform methods, and acquires the feature information of echo signal amplitude.
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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 (February 2014): 1002–18. http://dx.doi.org/10.1109/tgrs.2013.2246574.

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Більше джерел

Дисертації з теми "Sea clutter extraction"

1

Michelet, Jordan. "Extraction du fouillis de mer dans des images radar marin cohérent : modèles de champ de phases, méthodes de Boltzmann sur réseau, apprentissage." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS048.

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Nous nous intéressons au problème d’extraction du fouillis de mer dans des images radar marin. Le parti pris est de développer des méthodes de traitement d’image permettant de s’affranchir au mieux d’hypothèses sur la nature du fouillis de mer et du signal d’intérêt. D’une part, nous proposons un algorithme basé sur une approche variationnelle originale : un modèle multiphasique à interface diffuse. Les résultats obtenus montrent que l’algorithme est efficace lorsque le signal d’intérêt a un rapport signal-sur-fouillis suffisamment grand. D’autre part, nous nous intéressons à l’implémentation de schémas de Boltzmann sur réseau pour des problèmes de convection-diffusion à vitesse d’advection non constante et un terme source non nul. Nous décrivons le calcul de la consistance obtenue par analyse asymptotique à l’échelle acoustique et avec un opérateur de collision à temps de relaxation multiples, et étudions la stabilité de ces schémas dans un cas particulier. Les résultats obtenus montrent que les schémas proposés permettent de supprimer le bruit résiduel et de renforcer le signal d’intérêt sur l’image obtenue grâce à la première méthode. Enfin, nous proposons une méthode d’apprentissage permettant de s’affranchir d’hypothèses sur la nature du signal d’intérêt. En effet, en complément de l’algorithme par approche variationnelle, nous proposons un algorithme basé sur le traitement pulse-Doppler lorsque le signal d’intérêt est exo-clutter et a un rapport signal-sur-fouillis faible. Les résultats obtenus à partir du double auto-encodeur que nous proposons, étant comparables aux résultats fournis par chacune des deux méthodes, permettent de valider cette approche
We focus on the problem of sea clutter extraction in marine radar images. The aim is to develop image processing methods allowing us to avoid assumptions about the nature of the sea clutter and the signal of interest. On the one hand, we propose an original algorithm based on a variational approach : a multiphase model with diffuse interface. The results obtained show that the algorithm is efficient when the signal of interest has a sufficiently large signal-to-clutter ratio. On the other hand, we focus on the implementation of lattice Boltzmann schemes for convection-diffusion problems with non-constant advection velocity and non-zero source term. We describe the computation of the consistency obtained by asymptotic analysis at the acoustic scale and with a multiple relaxation time collision operator, and study the stability of these schemes in a particular case. The obtained results show that the proposed schemes allow removing the residual noise and to enhance the signal of interest on the image obtained with the first method. Finally, we propose a learning method allowing us to avoid assumptions on the nature of the signal of interest. Indeed, in addition to the variational approach, we propose an algorithm based on pulse-Doppler processing when the signal of interest is exo-clutter and has a low signal-to-clutter ratio. The results obtained from the proposed double auto-encoder, being comparable to the results provided by each of the two methods, allow validating this approach
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Тези доповідей конференцій з теми "Sea clutter extraction"

1

Yuan, Xujin, Yong Chen, Chao Wang, Hongcheng Yin, Jingping Yao, Zhiming Xu, and Yongge Lu. "A new correlation parameter extraction method for searching mode sea clutter restraint." In International Symposium on Optoelectronic Technology and Application 2014, edited by Gaurav Sharma, Fugen Zhou, and Jennifer Liu. SPIE, 2014. http://dx.doi.org/10.1117/12.2069459.

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2

Li, Yang, Xinyang Wang, Ning Zhang, Wenxing Wang, Qiming Zhang, Wenbo Ding, and Longshan Wu. "A Machine Learning Based First-Order Sea Clutter Region Extraction Method for HFSWR." In 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. IEEE, 2019. http://dx.doi.org/10.1109/apusncursinrsm.2019.8888570.

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3

Wu, Taifeng, Zhongtao Luo, Zishu He, Wang Zhaoyi, and Xuyuan Chen. "Sea-clutter region extraction based on image segmentation methods for over-the-horizon radar." In 2018 IEEE Radar Conference (RadarConf18). IEEE, 2018. http://dx.doi.org/10.1109/radar.2018.8378590.

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4

Wang, Yiwei, Bo Yin, Jinpeng Zhang, and Yushi Zhang. "Effective Sea Clutter Region Extraction Based on Improved YOLOv4 Algorithm for Shore-Based UHF-Band Radar." In 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ). IEEE, 2022. http://dx.doi.org/10.1109/iaeac54830.2022.9929877.

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5

Thayaparan, Thayananthan, Milos Darkovic, and Ljubisa Stankovic. "CFAR detection and extraction of maneuvering air target in strong sea-clutter via time-frequency-based S-method." In SPIE Defense, Security, and Sensing, edited by Kenneth I. Ranney and Armin W. Doerry. SPIE, 2009. http://dx.doi.org/10.1117/12.819361.

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6

Story, W. Rob, Thomas C. Fu, and Erin E. Hackett. "Radar Measurement of Ocean Waves." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49895.

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Анотація:
Over the past two decades a number of advances have been made in the use of radar systems for the measurement of ocean waves, building on early work at universities and the Naval Research Lab (NRL) to investigate the potential for extracting wave field measurements from the sea clutter seen in shipboard radar images. This early work was the foundation for modern wave radar systems, with hardware systems ranging from commercial off the shelf (COTS) incoherent navigation radar to specially developed, calibrated, coherent instrumentation radar and phased-array systems. Software algorithms and image analysis techniques have also been in constant development, which have evolved from 2D analysis of digitized images into modern techniques performing real-time 3D transformation of high resolution images. Most of these systems are being utilized to measure the directional wave spectra, with some systems also providing wave height estimates and sea surface elevation maps. More recently, the Naval Surface Warfare Center, Carderock Division (NSWCCD) and others have begun to utilize these techniques for shipboard measurement of open ocean waves. All these efforts have led to higher fidelity data, as well as data that were previously unobtainable. In this paper we provide an overview and history of the development of COTS incoherent wave radar systems, analysis techniques, and capabilities, from early characterization of sea clutter return to the latest developments in image inversion and sea surface topography. This review and summary provides a foundation on which to develop analysis techniques for the higher fidelity data, using lessons learned to improve future analysis. While not intending to be exhaustive, this paper seeks to highlight the insights gained from both historical and recent applications of these techniques, as well as the difficulties and issues associated with shipboard measurements such as ship motion, logistical constraints, and environmental factors.
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