Remote Sensing

Research

Jump to: Other

25 pages, 9994 KiB

Open AccessArticle

A Triple-Channel Network for Maritime Radar Targets Detection Based on Multi-Modal Features

by Kaiqi Wang and Zeyu Wang

Remote Sens. 2024, 16(24), 4662; https://doi.org/10.3390/rs16244662 - 13 Dec 2024

Cited by 1 | Viewed by 801

Abstract

Sea surface target detectors are often interfered by various complex sea surface factors such as sea clutter. Especially when the signal-to-clutter ratio (SCR) is low, it is difficult to achieve high-performance detection. This paper proposes a triple-channel network model for maritime target detection [...] Read more.

Sea surface target detectors are often interfered by various complex sea surface factors such as sea clutter. Especially when the signal-to-clutter ratio (SCR) is low, it is difficult to achieve high-performance detection. This paper proposes a triple-channel network model for maritime target detection based on the method of multi-modal data fusion. This method comprehensively improves the traditional multi-channel inputs by extracting highly complementary multi-modal features from radar echoes, namely, time-frequency image, phase sequence and correlation coefficient sequence. Appropriate networks are selected to construct a triple-channel network according to the internal data structure of each feature. The three features are utilized as the input of each network channel. To reduce the coupling between multi-channel data, the SE block is introduced to optimize the feature vectors of the channel dimension and improve the data fusion strategy. The detection results are output by the false alarm control unit according to the given probability of false alarm (PFA). The experiments on the IPIX datasets verify that the performance of the proposed detector is better than the existing detectors in dealing with complex ocean scenes. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Graphical abstract

25 pages, 94594 KiB

Open AccessArticle

Harbor Detection in Polarimetric SAR Images Based on Context Features and Reflection Symmetry

by Chun Liu, Jie Gao, Shichong Liu, Chao Li, Yongchao Cheng, Yi Luo and Jian Yang

Remote Sens. 2024, 16(16), 3079; https://doi.org/10.3390/rs16163079 - 21 Aug 2024

Cited by 1 | Viewed by 1009

Abstract

The detection of harbors presents difficulties related to their diverse sizes, varying morphology and scattering, and complex backgrounds. To avoid the extraction of unstable geometric features, in this paper, we propose an unsupervised harbor detection method for polarimetric SAR images using context features [...] Read more.

The detection of harbors presents difficulties related to their diverse sizes, varying morphology and scattering, and complex backgrounds. To avoid the extraction of unstable geometric features, in this paper, we propose an unsupervised harbor detection method for polarimetric SAR images using context features and polarimetric reflection symmetry. First, the image is segmented into three region types, i.e., water low-scattering regions, strong-scattering urban regions, and other regions, based on a multi-region Markov random field (MRF) segmentation method. Second, by leveraging the fact that harbors are surrounded by water on one side and a large number of buildings on the other, the coastal narrow-band area is extracted from the low-scattering regions, and the harbor regions of interest (ROIs) are determined by extracting the strong-scattering regions from the narrow-band area. Finally, by using the scattering reflection asymmetry of harbor buildings, harbors are identified based on the global threshold segmentation of the horizontal, vertical, and circular co- and cross-polarization correlation powers of the extracted ROIs. The effectiveness of the proposed method was validated with experiments on RADARSAT-2 quad-polarization images of Zhanjiang, Fuzhou, Lingshui, and Dalian, China; San Francisco, USA; and Singapore. The proposed method had high detection rates and low false detection rates in the complex coastal environment scenarios studied, far outperforming the traditional spatial harbor detection method considered for comparison. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Figure 1

24 pages, 37834 KiB

Open AccessArticle

Data Matters: Rethinking the Data Distribution in Semi-Supervised Oriented SAR Ship Detection

by Yimin Yang, Ping Lang, Junjun Yin, Yaomin He and Jian Yang

Remote Sens. 2024, 16(14), 2551; https://doi.org/10.3390/rs16142551 - 11 Jul 2024

Cited by 4 | Viewed by 1400

Abstract

Data, in deep learning (DL), are crucial to detect ships in synthetic aperture radar (SAR) images. However, SAR image annotation limitations hinder DL-based SAR ship detection. A novel data-selection method and teacher–student model are proposed in this paper to effectively leverage sparse labeled [...] Read more.

Data, in deep learning (DL), are crucial to detect ships in synthetic aperture radar (SAR) images. However, SAR image annotation limitations hinder DL-based SAR ship detection. A novel data-selection method and teacher–student model are proposed in this paper to effectively leverage sparse labeled data and improve SAR ship detection performance, based on the semi-supervised oriented object-detection (SOOD) framework. More specifically, we firstly propose a SAR data-scoring method based on fuzzy comprehensive evaluation (FCE), and discuss the relationship between the score distribution of labeled data and detection performance. A refined data selector (RDS) is then designed to adaptively obtain reasonable data for model training without any labeling information. Lastly, a Gaussian Wasserstein distance (GWD) and an orientation-angle deviation weighting (ODW) loss are introduced to mitigate the impact of strong scattering points on bounding box regression and dynamically adjusting the consistency of pseudo-label prediction pairs during the model training process, respectively. The experiments results on four open datasets have demonstrated that our proposed method can achieve better SAR ship detection performances on low-proportion labeled datasets, compared to some existing methods. Therefore, our proposed method can effectively and efficiently reduce the burden of SAR ship data labeling and improve detection capacities as much as possible. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Figure 1

22 pages, 13490 KiB

Open AccessArticle

Combined Coherent and Non-Coherent Long-Time Integration Method for High-Speed Target Detection Using High-Frequency Radar

by Gan Liu, Yingwei Tian, Biyang Wen and Chen Liu

Remote Sens. 2024, 16(12), 2139; https://doi.org/10.3390/rs16122139 - 13 Jun 2024

Cited by 1 | Viewed by 1994

Abstract

High-frequency (HF) radar plays a crucial role in the detection of far-range, stealth, and high-speed targets. Nevertheless, the echo signal of such targets typically exhibits a low signal-to-noise ratio (SNR) and significant amplitude fluctuations because their radar cross-section (RCS) accounting for the HF [...] Read more.

High-frequency (HF) radar plays a crucial role in the detection of far-range, stealth, and high-speed targets. Nevertheless, the echo signal of such targets typically exhibits a low signal-to-noise ratio (SNR) and significant amplitude fluctuations because their radar cross-section (RCS) accounting for the HF band is in the resonance region. While enhancing detection performance often requires long-time integration, existing algorithms inadequately consider the impact of amplitude fluctuation. In response to this challenge, this article introduces an improved approach based on coherent and non-coherent integration. Initially, coherent integration, employing the generalized Radon Fourier transform (GRFT), is utilized to derive a candidate detection set of targets’ range–time trajectories. This involves a joint solution for range migration (RM) and Doppler frequency migration (DFM) through a multi-parameter motion model search. Subsequently, the removal of low SNR pulses, followed by non-coherent integration, is implemented to mitigate amplitude fluctuation, referred to as Amplitude Fluctuation Suppression (AFS), and refine the detection outcomes. Both simulation and experiment results are provided to prove the effectiveness of the proposed AFS-GRFT algorithm. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Figure 1

22 pages, 7183 KiB

Open AccessArticle

A Grid-Based Gradient Descent Extended Target Clustering Method and Ship Target Inverse Synthetic Aperture Radar Imaging for UHF Radar

by Lizun Zhang, Hao Zhou, Liyun Bai and Yingwei Tian

Remote Sens. 2023, 15(23), 5466; https://doi.org/10.3390/rs15235466 - 23 Nov 2023

Cited by 1 | Viewed by 1558

Abstract

Inland shipping is of great significance in economic development, and ship surveillance and classification are of great importance for ship management and dispatch. For river ship detection, ultrahigh-frequency (UHF) radar is an effective equipment owing to its wide coverage and easy deployment. The [...] Read more.

Inland shipping is of great significance in economic development, and ship surveillance and classification are of great importance for ship management and dispatch. For river ship detection, ultrahigh-frequency (UHF) radar is an effective equipment owing to its wide coverage and easy deployment. The extension in range, Doppler, and azimuth and target recognition are two main problems in UHF ship detection. Clustering is a necessary step to get the center of an extended target. However, it is difficult to distinguish between different target echoes when they overlap each other in range, Doppler, and azimuth and so far practical methods for extended target recognition with UHF radar have been rarely discussed. In this study, a two-stage target classification method is proposed for UHF radar ship detection. In the first stage, grid-based gradient descent (GBGD) clustering is proposed to distinguish targets with three-dimensional (3D) information. Then in the second stage, the inverse synthetic aperture radar (ISAR) imaging algorithm is employed to differentiate ships of different types. The simulation results show that the proposed method achieves a 20% higher clustering accuracy than other methods when the targets have close 3D information. The feasibility of ISAR imaging for target classification using UHF radar is also validated via simulation. Some experimental results are also given to show the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Graphical abstract

20 pages, 5160 KiB

Open AccessArticle

Sea Clutter Suppression Using Smoothed Pseudo-Wigner–Ville Distribution–Singular Value Decomposition during Sea Spikes

by Guigeng Li, Hao Zhang, Yong Gao and Bingyan Ma

Remote Sens. 2023, 15(22), 5360; https://doi.org/10.3390/rs15225360 - 15 Nov 2023

Cited by 5 | Viewed by 1833

Abstract

The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves [...] Read more.

The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves and breaking waves. This severely affects the radar target detection performance. This paper proposes a smoothed pseudo-Wigner–Ville distribution–singular value decomposition (SPWVD-SVD) method for sea clutter suppression. This method determines the instantaneous frequency range of the target by contrasting the time–frequency characteristics of the sea spike and the target. Subsequently, it employs a singular value difference spectrum to reduce the rank of the Hankel matrix, thereby reducing the computational burden of the instantaneous frequency estimation step in the experiment. Based on the instantaneous frequency range of the target in the time–frequency domain, the singular values of the target signal are retained, while the singular values of clutter are set to zero. This process accomplishes the reconstruction of radar echo signals and effectively achieves the suppression of sea clutter. The suppression effect is verified using simulation data alongside ten sets of Intelligent Pixel processing X-band (IPIX) radar data against the background of sea spikes. By contrasting the clutter amplitudes before and after suppression, the SPWVD-SVD algorithm demonstrated an average clutter suppression of 15.06 dB, which proves the effectiveness of the proposed algorithm in suppressing sea clutter. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Graphical abstract

20 pages, 7148 KiB

Open AccessArticle

Wind Direction Extraction from X-Band Marine Radar Images Based on the Attenuation Horizontal Component

by Huanyu Yu, Zhizhong Lu and Hui Wang

Remote Sens. 2023, 15(16), 3959; https://doi.org/10.3390/rs15163959 - 10 Aug 2023

Viewed by 1784

Abstract

This paper presents a novel algorithm based on the attenuation horizontal component for wind direction retrieval from X-band marine radar images. The range dependence of radar return on the ocean surface can be presented in radar images, and the radar return decreases with [...] Read more.

This paper presents a novel algorithm based on the attenuation horizontal component for wind direction retrieval from X-band marine radar images. The range dependence of radar return on the ocean surface can be presented in radar images, and the radar return decreases with the increase in range. The traditional curve-fitting method averages the radar return of the whole range to retrieve the wind direction, but it is vulnerable to the interference of fixed objects and long-range low-intensity pixel points. For the pixels with the same range in the polar coordinates of the radar image, the ideal range attenuation model is derived by selecting the pixels with the highest intensity value. The ideal attenuation model is used to fit the attenuation data and calculate the attenuation horizontal component at each azimuth direction. To eliminate the effect of outliers, the iterative optimization method is used in the estimation of the attenuation horizontal component and the weights of the data are continuously updated. Finally, the wind direction is determined based on the azimuthal dependence of the attenuation horizontal component. This algorithm was tested using shipboard radar images and anemometer data collected in the East China Sea. The results show that, compared with the single curve-fitting method, the proposed algorithm can improve the wind direction retrieval accuracy in the case of more fixed targets. Under the condition of more fixed targets, the deviation and root mean square error are reduced by 16.3° and 16.2°, respectively. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Figure 1

Other

Jump to: Research

17 pages, 10872 KiB

Open AccessTechnical Note

A Floating Small Target Identification Method Based on Doppler Time Series Information

by Hengli Yu, Hao Ding, Zheng Cao, Ningbo Liu, Guoqing Wang and Zhaoxiang Zhang

Remote Sens. 2024, 16(3), 505; https://doi.org/10.3390/rs16030505 - 28 Jan 2024

Viewed by 1482

Abstract

Traditional radar detection methods heavily rely on the signal-to-clutter ratio (SCR); a variety of feature-based detection methods have been proposed, providing a new way for radar detection and the recognition of weak targets. Existing feature-based detection methods determine the presence or absence of [...] Read more.

Traditional radar detection methods heavily rely on the signal-to-clutter ratio (SCR); a variety of feature-based detection methods have been proposed, providing a new way for radar detection and the recognition of weak targets. Existing feature-based detection methods determine the presence or absence of a target based on whether the feature value is within the judgment region, generally focusing only on the distribution of features and making insufficient use of inter-feature chronological information. This paper uses the autoregressive (AR) model to model and predict the time sequence of radar echoes in the feature domain and takes the chronological information of historical frame features as the prior information to form new features for detection on this basis. A classification method for floating small targets based on the Doppler spectrum centroid sequence is proposed. By using the AR model to fit the Doppler spectrum centroid feature sequence of the target, the model coefficients are regarded as the secondary features for target identification. The measured data show that the correct classification and identification rate of this method for ship targets and floating small targets can reach over 92% by using 50 centroid features. Full article

(This article belongs to the Special Issue Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Application of Shore-Based, Sky-Based and Marine Radars to Ocean and Target Parameter Extraction

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI