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Relevant bibliographies by topics / Sea clutter extraction

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Journal articles

Academic literature on the topic 'Sea clutter extraction'

Author: Grafiati

Published: 16 September 2023

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Journal articles on the topic "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 (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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