SVM Based Land/Sea Clutter Classification Algorithm

Jin, Zhen; Zhao, Chun Hui; Zhou, W

doi:10.4028/www.scientific.net/amm.236-237.1156

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Therefore, identification and localization of clutter and interference regions in the RD spectrum is the first step. Conventional detection of clutter or interference regions is achieved by image segmentation or classifier classification [18][19][20], and detection performance depends on many artificial factors. However, the deep-learning-based Faster R-CNN detection framework [21], which can simultaneously localize and classify the target in less time through learning, has a good detection effect on normal-sized targets.…”

Section: Introductionmentioning

confidence: 99%

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

Zhang

Niu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-frequency surface wave radar (HFSWR) is of great significance for maritime detection, but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and interference, making it difficult to detect vessels. In this paper, we propose an intelligent detection algorithm for targets concealed in strong clutter and complex interference environments. The algorithm has two stages: preprocessing and target detection. In the preprocessing stage, faster region-based convolutional neural networks (Faster R-CNN) are designed to identify and locate clutter and interference regions in the range Doppler (RD) spectrum; in the target detection stage, a two-level cascade algorithm is proposed. First, an extremum detection algorithm is proposed to identify suspicious target points in the clutter/interference regions, including real and false target points, to quickly obtain potential target positions. Second, in consideration of the characteristics of radar targets, two lightweight networks are designed to extract the convolutional neural network (CNN) features and the stacked auto-encoder (SAE) features of the potential target locations. Then, fusion features are obtained and sent to an extreme learning machine (ELM) that acts as a second-level classifier to distinguish between real and false target points. Experiments show that the proposed HFSWR target-detection algorithm has better performance for vessel detection in clutter/interference regions than the current mainstream detection algorithms.

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Section: Introductionmentioning

confidence: 99%

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

Zhang

Niu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This method overcomes the inaccuracy caused by subjective selection of

α

and implements the algorithm's self‐adaptation to clutter environment. Jin Zhenlu and others [5] proposed a multi‐feature recognition algorithm for land/sea clutter based on support vector machine (SVM). The land/sea clutter echo signal model was established by using SVM to improve the land/sea clutter recognition rate.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the algorithms in literature [3] and literature [4] have the disadvantages of a complex model and a large amount of computation. In the literature [5] and literature [6], the application of SVM in radar clutter recognition has the disadvantages of low recognition accuracy and long training time.…”

Section: Introductionmentioning

confidence: 99%

Research on radar clutter recognition method based on LSTM

Song

Wang

Xie

2019

J. eng.

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“…The differences in the backscatter coefficient of topographic features and the sea have been investigated as a way to improve the geolocation accuracy of targets via a process known as coordinate registration. This is the process of transforming from the radar (or group) range to the ground range to obtain the geographic coordinates of an object observed by a radar [15]- [18]. It has been found that the difference in the backscatter coefficient of certain topographic features could be suitable for coordinate registration.…”

mentioning

confidence: 99%

High Frequency Land Backscatter Coefficients Over Northern Australia and the Effects of Various Surface Properties

Edwards

Cervera

MacKinnon

2022

IEEE Trans. Antennas Propagat.

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Over-the-horizon radars (OTHRs) utilize the refractive properties of the ionosphere to illuminate targets beyond the Earth's horizon. Consequently, the performance of this type of radar is highly dependent on the ionosphere. Reliable models of the radar ground backscatter are required to accurately assess the ionospheric propagation conditions and, thus, the expected performance of OTHRs. The ground backscatter coefficient characterizes the amount of radiation scattered back from a surface toward a receiver per unit area. While the backscatter coefficient of the sea is well understood and may be calculated from theory if the sea state is known, the backscatter coefficient of land at high frequencies is not well understood. To calculate the land backscatter coefficients over Northern Australia, a methodology that compares observed backscatter ionograms to those synthesized using high-frequency (HF) radio wave raytracing techniques through model ionospheres was developed. Maps of the backscatter coefficients across Northern Australia were produced. The effects of surface properties, including topography, soil moisture, and vegetation cover on the backscatter coefficients, were investigated. A weak positive correlation between the backscatter coefficient and the soil moisture and surface roughness was observed; however, it was found that the vegetation had the largest effect on the backscatter coefficient.

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SVM Based Land/Sea Clutter Classification Algorithm

Cited by 10 publications

References 9 publications

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

Research on radar clutter recognition method based on LSTM

High Frequency Land Backscatter Coefficients Over Northern Australia and the Effects of Various Surface Properties

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