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Special Issue "Radar Signal Processing and Imaging for Ocean Remote Sensing"

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

Deadline for manuscript submissions: 30 August 2023 | Viewed by 5230

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Special Issue Editors

Prof. Dr. Xiaoqing Wang

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Guest Editor

School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, China
Interests: ocean remote sensing; radar signal processing; rader signal imaging

Prof. Dr. Jinsong Chong

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Guest Editor

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2. National Key Laboratory of Microwave Imaging Technology, Beijing 100190, China
Interests: ocean remote sensing; target detection; signal processing

Prof. Dr. Yunhua Wang
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Guest Editor

1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2. Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Interests: asymptotic and numerical simulations of electromagnetic wave scattering; ocean microwave remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Liu

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Guest Editor

Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100190, China
Interests: spaceborne radar system design; ocean microwave scattering; ocean features extracting

Prof. Dr. Ali Khenchaf

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Guest Editor

Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: computer science; engineering; observation; propagation; wave scattering; scattering in random media; monostatic and bistatic scattering; electromagnetic radar cross section; sea clutter; active and passive sensors (radar, lidar, optics, GNSS); radar applications; data assimilation (n-D); sea surface and environment; extraction of parameters from the observed scene: imagery and target parameter estimation; direct and inverse problems; remote sensing of the ocean and the environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For several decades, radar sensors have been used for sea surface observations at a range of spatial and temporal scales. Marine radar remote sensing, with its ever-increasing technological maturity, has become a widely validated method to detect and study the ocean. For instance, satellite altimeters conduct accurate daily measurements of changes in elevation of the ocean surface, synthetic aperture radar (SAR) maps internal waves and oil spills, radar scatterometers estimate sea surface winds, and the spaceborne spectrometer SWIM (Surface Wave Investigation and Monitoring) measures the global wave direction spectrum. These phenomena are mainly the results of variation in the characteristics of the upper water layers and modulation of short wind waves on the sea surface, which are mainly responsible for imaging the phenomena in signals of radar remote sensing instruments. Although significant progress has been made in ocean remote sensing, many aspects of ocean surface electromagnetic wave scattering and the mechanisms of signal and imaging of oceanic/atmospheric phenomena are still poorly understood.

This Special Issue is focused on the latest developments in radar signal processing and imaging for ocean remote sensing. We encourage submissions on the theory and modeling of ocean microwave scattering and radar signal processing, as well as field and laboratory experiments, including but not limited to the following topics:

Design and investigation of radar signal processing and imaging algorithms for ocean remote sensing;
Radar remote sensing of sea surface winds, waves, currents, sea ice, internal wave, oil spill and other features;
Radar remote sensing detections or recognitions of hard targets (ships, oil rigs, etc.);
Radar remote sensing image segmentation and classification in coastal environments;
Innovative or improved methods and algorithms for radar remote sensing for oceanography applications;
Algorithms or schemes for the retrieval of key surface parameters of the lower atmosphere or the upper ocean;
Application of artificial intelligence for pre- and post-processing remotely sensed data and signals.

Prof. Dr. Xiaoqing Wang
Prof. Dr. Jinsong Chong
Prof. Dr. Yunhua Wang
Dr. Lei Liu
Prof. Dr. Ali Khenchaf
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 2500 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

ocean remote sensing
radar signal processing
radar imaging
ocean numerical modeling
artificial intelligence

Published Papers (7 papers)

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Research

Open AccessArticle

Fast Factorized Backprojection Algorithm in Orthogonal Elliptical Coordinate System for Ocean Scenes Imaging Using Geosynchronous Spaceborne–Airborne VHF UWB Bistatic SAR

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Remote Sens. 2023, 15(8), 2215; https://doi.org/10.3390/rs15082215 - 21 Apr 2023

Viewed by 464

Abstract

Geosynchronous (GEO) spaceborne–airborne very high-frequency ultra-wideband bistatic synthetic aperture radar (VHF UWB BiSAR) can conduct high-resolution and wide-swath imaging for ocean scenes. However, GEO spaceborne–airborne VHF UWB BiSAR imaging faces some challenges such as the geometric configuration, huge amount of echo data, serious range–azimuth coupling, large spatial variance, and complex motion error, which increases the difficulty of the high-efficiency and high-precision imaging. In this paper, we present an improved bistatic fast factorization backprojection (FFBP) algorithm for ocean scene imaging using the GEO satellite-unmanned aerial vehicle (GEO-UAV) VHF UWB BiSAR, which can solve the above issues with high efficiency and high precision. This method reconstructs the subimages in the orthogonal elliptical polar (OEP) coordinate system based on the GEO satellite and UAV trajectories as well as the location of the imaged scene, which can further reduce the computational burden. First, the imaging geometry and signal model of the GEO-UAV VHF UWB BiSAR are established, and the construction of the OEP coordinate system and the subaperture imaging method are proposed. Moreover, the Nyquist sampling requirements for the subimages in the OEP coordinate system are derived from the range error perspective, which can offer a near-optimum tradeoff between precision and efficiency. In addition, the superiority of the OEP coordinate system is analyzed, which demonstrates that the angular dimensional sampling rate of the subimages is significantly reduced. Finally, the implementation processes and computational burden of the proposed algorithm are provided, and the speed-up factor of the proposed FFBP algorithm compared with the BP algorithm is derived and discussed. Experimental results of ideal point targets and natural ocean scenes demonstrate the correctness and effectiveness of the proposed algorithm, which can achieve near-optimal imaging performance with a low computational burden. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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Open AccessArticle

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

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Remote Sens. 2023, 15(8), 2078; https://doi.org/10.3390/rs15082078 - 14 Apr 2023

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Abstract

The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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Open AccessArticle

A Partial Reconstruction Method for SAR Altimeter Coastal Waveforms Based on Adaptive Threshold Judgment

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Remote Sens. 2023, 15(6), 1717; https://doi.org/10.3390/rs15061717 - 22 Mar 2023

Viewed by 532

Abstract

Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the retracking methods of traditional coastal waveforms difficult to apply. This study proposes a partial reconstruction method for SAR altimeter coastal waveforms. By making adaptive threshold judgments of model matching errors and repairing the contaminated waveforms based on the nearest linear prediction, the success rate of retracking and retrieval precision of SSH are significantly improved. The data from the coastal experimental areas of the Sentinel-3B satellite altimeter are processed. The results indicate that the mean proportion of waveform quality improvement brought by partial reconstruction is 80.30%, the mean retracking success rate of reconstructed waveforms is 85.60%, and the mean increasing percentage is 30.98%. The noise levels of SSH data retrieved by different methods are calculated to evaluate the processing precision. It is shown that the 20 Hz SSH precisions of the original and reconstructed coastal waveforms are 12.75 cm and 6.32 cm, respectively, and the corresponding 1 Hz SSH precisions are 2.85 cm and 1.41 cm, respectively. The results validate that the proposed partial reconstruction method has improved the SSH precision by a factor of two, and the comparison results with mean sea surface (MSS) model data further verify this conclusion. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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Open AccessCommunication

Impact of SAR Azimuth Ambiguities on Doppler Velocity Estimation Performance: Modeling and Analysis

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Remote Sens. 2023, 15(5), 1198; https://doi.org/10.3390/rs15051198 - 22 Feb 2023

Viewed by 587

Abstract

Doppler Centroid Analysis (DCA) technique is one of the major techniques that do permit a direct retrieval of ocean surface velocity from synthetic aperture radar (SAR) data. However, azimuth ambiguities in the SAR images severely restrict the capability of DCA technique to obtain accurate ocean surface Doppler velocities. Therefore, it is necessary to investigate how the azimuth ambiguities impact the Doppler velocity estimation performance and to evaluate how significant the impact is. In this paper, a model for ocean surface Doppler velocity estimation affected by azimuth ambiguities is developed resorting to jointly circular Gaussian processes, and its statistic is derived. The impact of azimuth ambiguities on Doppler velocity estimation performance in terms of Doppler centroid estimation bias and the standard deviation of Doppler centroid estimates is analyzed. The theoretical results are validated through simulation and Doppler velocities retrieved from Chinese Gaofen-3 (GF-3) SAR Doppler centroid estimates affected by azimuth ambiguities. This study will help researchers better understand the impact of azimuth ambiguities on Doppler velocity estimation, and will provide a theoretical reference for subsequent research on how to reduce the impact of azimuth ambiguities more effectively. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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Open AccessArticle

Effects of Wave-Induced Doppler Velocity on the Sea Surface Current Measurements by Ka-Band Real-Aperture Radar with Small Incidence Angle

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Remote Sens. 2023, 15(4), 1127; https://doi.org/10.3390/rs15041127 - 18 Feb 2023

Viewed by 767

Abstract

The Doppler shift of microwave radar sea surface echoes serves as the foundation for sea surface current field retrieval; it includes the shift caused by satellite platform motion, ocean waves, and sea surface currents. The Doppler shift caused by ocean waves is known as the wave-induced Doppler velocity (U_WD), and its removal is critical for the accurate retrieval of sea surface current fields. The low-incidence Ka-band real-aperture radar rotary scan regime has the capability of directly observing wide-swath two-dimensional current fields, but as a new regime to be further explored and validated, simulation and analysis of U_WD in this regime have a significant influence on the hardware design and currently observed applications of this satellite system in its conceptual stage. In this study, we simulated and investigated the impacts of radar parameters and sea-state conditions on the U_WD obtained from small-incidence-angle Ka-band rotational scanning radar data and verified the simulation results with the classical analytical solution of average specular scattering point velocity. Simulation results indicate that the change in the azimuth direction of platform observation affects U_WD accuracy. Accuracy is the lowest when the antenna is in a vertical side-view. The U_WD increases slowly with the incidence angle. Ocean waves are insensitive to polarization in the case of small-incidence-angle specular scattering. The increase in wind speed and the development of wind waves result in a substantial increase in U_WD. We classified swell by wavelength and wave height and found that U_WD increases with swell size, especially the contribution of swell height to U_WD, which is more significant. The contribution of the swell to U_WD is smaller than that of wind waves to U_WD. Furthermore, the existence of sea surface currents changes the contribution of ocean waves to U_WD, and the contribution weakens with increasing wind speed and increases with wind wave development. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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Open AccessArticle

Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images

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Remote Sens. 2023, 15(3), 673; https://doi.org/10.3390/rs15030673 - 23 Jan 2023

Cited by 4 | Viewed by 731

Abstract

Due to the limitations of the horizontal bounding boxes for locating the oriented ship targets in synthetic aperture radar (SAR) images, the rotated bounding box (RBB) has received wider attention in recent years. First, the existing RBB encodings suffer from boundary discontinuity problems, which interfere with the convergence of the model, and then lead to some problems, such as the inaccurate location of the ship targets in the boundary state. Thus, from the perspective that the long-edge features of the ships are more representative of their orientation, the long-edge decomposition RBB encoding has been proposed in this paper, which can avoid the boundary discontinuity problem. Second, the problem of the positive and negative samples imbalance is serious for the SAR ship images because only a few ship targets exist in the vast background of these images. Since the ship targets of different sizes are subject to varying degrees of interference caused by this problem, a multiscale elliptical Gaussian sample balancing strategy has been proposed in this paper, which can mitigate the impact of this problem by labeling the loss weights of the negative samples within the target foreground area with multiscale elliptical Gaussian kernels. Finally, experiments based on the CenterNet model were implemented on the benchmark SAR image dataset SSDD (SAR ship detection dataset). The experimental results demonstrate that our proposed long-edge decomposition RBB encoding outperforms other conventional RBB encodings in the task of oriented ship detection in SAR images. In addition, our proposed multiscale elliptical Gaussian sample balancing strategy is effective and can improve the model performance. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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Open AccessArticle

Analysis of Wave Breaking on Gaofen-3 and TerraSAR-X SAR Image and Its Effect on Wave Retrieval

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Remote Sens. 2023, 15(3), 574; https://doi.org/10.3390/rs15030574 - 18 Jan 2023

Viewed by 754

Abstract

The main purpose of our work is to investigate the performance of wave breaking and its effect on wave retrieval in data acquired from the Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) at C-band and the German TerraSAR-X (TS-X) at X-band. The SAR images available for this study included 140 GF-3 images acquired in quad-polarization strip (QPS) mode and 50 dual-polarized (vertical-vertical (VV) and horizontal-horizontal (HH)) TS-X images acquired in stripmap (SM) mode. Moreover, these images were collocated with the waves simulated by the numeric WAVEWATCH-III (WW3) (version 5.16) model and HYbrid Coordinate Ocean Model (HYCOM) current. In particular, a few images covered the moored buoys monitored by the National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA). The comparison between the WW3-simulated results and the significant wave heights (SWHs) from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA-5) showed that the correlation coefficient (COR) was 0.4–0.6 with a root mean squared error (RMSE) of about 0.2 m at SWHs of 0–4 m. The winds were inverted using VV-polarized geophysical model functions (GMFs), e.g., CSARMOD-GF for the GF-3 images and XMOD2 for the TS-X images. The Bragg resonant roughness in the normalized radar cross section (NRCS) was simulated using a radar backscattering model and the SAR-derived wind, WW3-simulated wave parameters, and HYCOM current. Then, the contribution of the non-polarized (NP) wave breaking to the SAR data was estimated by the VV-polarized NRCS, the HH-polarized NRCS, and the polarization ratio (PR) of the co-polarized Bragg resonant components in the NRCS. Because co-polarized Bragg resonant components in the NRCSs have poor results, due to the saturation for wind speeds greater than 20 m/s, the analysis of wave breaking is excluded at such conditions. The results revealed that the backscattering signal in the C-band was more sensitive to wave breaking than the backscattering signal in the X-band. Interestingly, the ratio had a linear correlation with wind speed. Moreover, the variation in the bias (inverted SWH minus WW3 simulation) showed that the bias increased as the wind speed (>8 m/s) and whitecap coverage (>0.005) increased. Following this rationale, wave retrieval during tropical cyclones should consider the influence of wave breaking. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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