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Remote Sensing
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Target Detection with Fully-Polarized Radar
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Target Detection with Fully-Polarized Radar

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 4790

Special Issue Editors

Prof. Dr. Siwei Chen

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Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: radar polarimetry; polarimetric radar imaging; environmental study; machine learning
Prof. Dr. Tao Liu

E-Mail Website
Guest Editor
School of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
Interests: radar polarization; polarization information process; SAR ship detection; polarimetric wave design
Prof. Dr. Dusan Gleich

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia
Interests: synthetic aperture radar image enhancement; small-radar development; deep learning for SAR image enhancement; data interpretation, short-range radar development, radar signal processing, through the wall imaging, soil moisture estimation, and machine vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gui Gao

E-Mail Website
Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, No. 999 Xi’an Road, Pidu Zone, Chengdu 611756, China
Interests: radar signal processing; SAR target detection; marine environment; SAR GMTI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Yang

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: radar polarimetry; feature extraction; target detection and target classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Polarization is an essential characteristic of electromagnetic waves. By introducing polarization information, radar systems can obtain more comprehensive target information, which effectively enhances their performance in target detection, anti-interference and recognition. Fully polarized radar has broad application prospects in fields such as reconnaissance, space surveillance and meteorological observation.

In this vein, a timely collection of advanced concepts, techniques and applications is essential for the development of target detection with fully polarized radar and future remote sensing studies.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Recent advances in polarimetric feature extraction;
  • Recent advances in polarimetric scattering mechanism understanding;
  • Recent advances in stealthy, slow, or high-speed target detection using polarization information;
  • Recent advances in PolSAR target detection and recognition;
  • Advanced deep learning techniques applied in fully polarized radar;
  • Space–time adaptive processing including polarization information;
  • Other aspects in polarimetric radar signal processing and information extraction.

Prof. Dr. Siwei Chen
Prof. Dr. Tao Liu
Prof. Dr. Dusan Gleich
Prof. Dr. Gui Gao
Prof. Dr. Jian Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • target detection
  • polarimetry
  • fully polarized radar
  • deep learning
  • ship wake detection
  • stealthy, slow, or high-speed targets
  • clutter

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Published Papers (4 papers)

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Research

20 pages, 9670 KiB  
Article
A Two-Component Polarimetric Target Decomposition Algorithm with Grassland Application
by Pingping Huang, Yalan Chen, Xiujuan Li, Weixian Tan, Yuejuan Chen, Xiangli Yang, Yifan Dong, Xiaoqi Lv and Baoyu Li
Remote Sens. 2024, 16(15), 2832; https://doi.org/10.3390/rs16152832 - 2 Aug 2024
Viewed by 577
Abstract
The study of the polarimetric target decomposition algorithm with physical scattering models has contributed to the development of the field of remote sensing because of its simple and clear physical meaning with a small computational effort. However, most of the volume scattering models [...] Read more.
The study of the polarimetric target decomposition algorithm with physical scattering models has contributed to the development of the field of remote sensing because of its simple and clear physical meaning with a small computational effort. However, most of the volume scattering models in these algorithms are for forests or crops, and there is a lack of volume scattering models for grasslands. In order to improve the accuracy of the polarimetric target decomposition algorithm adapted to grassland data, in this paper, a novel volume scattering model is derived considering the characteristics of real grassland plant structure and combined with the backward scattering coefficients of grass, which is abstracted as a rotatable ellipsoid of variable shape. In the process of rotation, the possibility of rotation is considered in two dimensions, the tilt angle and canting angle; for particle shape, the anisotropy degree A is directly introduced as a parameter to describe and expand the applicability of the model at the same time. After obtaining the analytical solution of the parameters and using the principle of least negative power to determine the optimal solution of the model, the algorithm is validated by applying it to the C-band AirBorne dataset of Hunshandak grassland in Inner Mongolia and the X-band Cosmos-Skymed dataset of Xiwuqi grassland in Inner Mongolia. The performance of the algorithm with five polarimetric target decomposition algorithms is studied comparatively. The experimental results show that the algorithm proposed in this paper outperforms the other algorithms in terms of grassland decomposition accuracy on different bands of data. Full article
(This article belongs to the Special Issue Target Detection with Fully-Polarized Radar)
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22 pages, 1628 KiB  
Article
Optimization on the Polarization and Waveform of Radar for Better Target Detection Performance under Rainy Condition
by Xinda Li, Xu Cheng, Xinjie Ju, Yunli Peng, Jinzhu Hu and Jianbing Li
Remote Sens. 2024, 16(14), 2557; https://doi.org/10.3390/rs16142557 - 12 Jul 2024
Viewed by 760
Abstract
Under rainy conditions, atmospheric settling particles have significant depolarization effects on radar waves and thus affect the target detection performance. This paper establishes an extended target detection model for fully polarized radar under complex rainy conditions and proposes two polarization and waveform optimization [...] Read more.
Under rainy conditions, atmospheric settling particles have significant depolarization effects on radar waves and thus affect the target detection performance. This paper establishes an extended target detection model for fully polarized radar under complex rainy conditions and proposes two polarization and waveform optimization methods by taking into account the transmission effect of rainfall. If the rainfall information is known, the polarization and waveform for transmitting and receiving are optimized by maximizing the system Signal-to-Clutter/Noise Ratio (SCNR). While the rainfall information is absent, the optimal transmitting polarization and waveform are obtained by directly compensating the transmission effect from non-rainfall optimization results using fully polarized radar observation parameters. Using measured data from the T-72 tank, The experimental results verify the effectiveness and robustness of the two methods in real scenarios and 4 dB on the improvement of SCNR can be achieved. Full article
(This article belongs to the Special Issue Target Detection with Fully-Polarized Radar)
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22 pages, 13998 KiB  
Article
A Novel Polarization Scattering Decomposition Model and Its Application to Ship Detection
by Lu Fang, Ziyuan Yang, Wenxing Mu and Tao Liu
Remote Sens. 2024, 16(1), 178; https://doi.org/10.3390/rs16010178 - 31 Dec 2023
Viewed by 1249
Abstract
In polarimetric synthetic aperture radar (POLSAR), it is of great significance for civil and military applications to find novel model-based decomposition methods suitable for ship detection in different detection backgrounds. Based on the physical interpretation of polarimetric decomposition theory and the Lasso rule [...] Read more.
In polarimetric synthetic aperture radar (POLSAR), it is of great significance for civil and military applications to find novel model-based decomposition methods suitable for ship detection in different detection backgrounds. Based on the physical interpretation of polarimetric decomposition theory and the Lasso rule for sparse features, we propose a four-component decomposition model, which is composed of surface scattering (Odd), double-bounce scattering (Dbl), volume scattering (Vol), and ±45° oriented dipole (Od). In principle, the Od component can describe the compounded scattering structure of a ship consisting of odd-bounce and even-bounce reflectors. Moreover, the pocket perceptron learning algorithm (PPLA) and support vector machine (SVM) are utilized to solve the linear inseparable problems in this study. Using large amounts of RADARSAT-2 (RS-2) fully polarized SAR data and AIRSAR data, our experimental results show that the Od component can make a great contribution to ship detection. Compared with other conventional decomposition methods used in the experiments, the proposed four-component decomposition method has better performance and is more effective and feasible to detect ships. Full article
(This article belongs to the Special Issue Target Detection with Fully-Polarized Radar)
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18 pages, 6454 KiB  
Article
Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization
by Rui Guo, Xiaopeng Zhao, Guanzhong Zuo, Ying Wang and Yi Liang
Remote Sens. 2023, 15(19), 4802; https://doi.org/10.3390/rs15194802 - 1 Oct 2023
Cited by 1 | Viewed by 1380
Abstract
The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is [...] Read more.
The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is proposed which optimizes the semantic segmentation metric intersection over union (IOU) instead of using the traditional cross-entropy loss. Meanwhile, making use of the advantages of the dual-path structure, the network extracts feature through the spatial path (SP) and the context path (CP) to achieve a balance between efficiency and accuracy. Aiming at a polarimetric SAR (PolSAR) image, the proposed network is conducted on the PolSAR datasets for terrain segmentation. Compared to the typical dual-path network, which is the bilateral segmentation network (BiSeNet), the proposed LoSARNet can obtain better mean intersection over union (MIOU). And the proposed network also shows the highest evaluation index and the best performance when compared with several typical networks. Full article
(This article belongs to the Special Issue Target Detection with Fully-Polarized Radar)
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