Remote Sensing

Research

29 pages, 12305 KiB

Open AccessArticle

APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

by Yaning Wang, Haibo Yu, Ling Zhang and Gangsheng Li

Remote Sens. 2023, 15(15), 3818; https://doi.org/10.3390/rs15153818 - 31 Jul 2023

Cited by 1 | Viewed by 1179

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed [...] Read more.

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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38 pages, 14304 KiB

Open AccessArticle

Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation

by Yunfeng Zhang, Yiming Wang, Yonggang Ji and Ming Li

Remote Sens. 2023, 15(11), 2952; https://doi.org/10.3390/rs15112952 - 5 Jun 2023

Viewed by 1567

Abstract

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, [...] Read more.

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT). Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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20 pages, 8464 KiB

Open AccessArticle

First Results from the WindRAD Scatterometer on Board FY-3E: Data Analysis, Calibration and Wind Retrieval Evaluation

by Zhen Li, Anton Verhoef, Ad Stoffelen, Jian Shang and Fangli Dou

Remote Sens. 2023, 15(8), 2087; https://doi.org/10.3390/rs15082087 - 15 Apr 2023

Cited by 10 | Viewed by 1873

Abstract

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The diversity of the radar view geometry varies across the swaths, with maximum diversity in the [...] Read more.

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The diversity of the radar view geometry varies across the swaths, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence, NOCant is applied for the analysis of wind retrieval from the Ku-band and C-band. An extra engineering correction of 0.15 dB and 0.20 dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. In addition, the instrument backscatter data stability over time is good, and the retrieved winds can fulfill operational requirements. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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18 pages, 21221 KiB

Open AccessArticle

Quantitative Inversion Ability Analysis of Oil Film Thickness Using Bright Temperature Difference Based on Thermal Infrared Remote Sensing: A Ground-Based Simulation Experiment of Marine Oil Spill

by Meiqi Wang, Junfang Yang, Shanwei Liu, Jie Zhang, Yi Ma and Jianhua Wan

Remote Sens. 2023, 15(8), 2018; https://doi.org/10.3390/rs15082018 - 11 Apr 2023

Cited by 4 | Viewed by 1876

Abstract

Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response [...] Read more.

Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response and risk assessment of oil spills. In recent years, thermal infrared remote sensing has been gradually applied to quantify the OFT. In this paper, the outdoor oil spill simulation experiments were designed, and the bright temperature (BT) data of different OFTs were obtained for 24 consecutive hours in summer and autumn. On the basis of the correlation analysis of OFT and bright temperature difference (BTD) between oil and water, the traditional regression fitting model, classical machine learning model, ensemble learning model, and deep learning model were applied to the inversion of OFT. At the same time, inversion results of the four models were compared and analyzed. In addition, the best OFT inversion time using thermal infrared was studied based on 24-h thermal infrared data. Additionally, the inversion results were compared with the measured results; the optimal OFT range detectable using thermal infrared was explored. The experimental results show that: (1) Compared with ensemble learning model, traditional regression fitting model, and classical machine learning model, Convolutional Neural Network (CNN) has the advantages of high stability while maintaining high-precision inversion, and can be used as the preferred model for oil film thickness inversion; (2) The optimal time for OFT detection is around 10:00 to 13:00 of the day, and is not affected by seasonal changes; (3) During the day, thermal infrared has good detection ability for OFT greater than 0.4 mm, and weak detection ability for thinner oil films; (4) At night, thermal infrared has certain detection ability for relatively thick oil film, but the accuracy is lower than that in the daytime. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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23 pages, 13759 KiB

Open AccessArticle

Offshore Hydrocarbon Exploitation Target Extraction Based on Time-Series Night Light Remote Sensing Images and Machine Learning Models: A Comparison of Six Machine Learning Algorithms and Their Multi-Feature Importance

by Rui Ma, Wenzhou Wu, Qi Wang, Na Liu and Yutong Chang

Remote Sens. 2023, 15(7), 1843; https://doi.org/10.3390/rs15071843 - 30 Mar 2023

Cited by 3 | Viewed by 1959

Abstract

The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning [...] Read more.

The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning models has become one of the most novel techniques for OHE target extraction in recent years. Its performance is mainly influenced by machine learning models, target features, and regional differences. However, there is still a lack of internal comparative studies on the different influencing factors in this framework. Therefore, based on this framework, we selected four different typical experimental regions within the hydrocarbon basins in the South China Sea to validate the extraction performance of six machine learning models (the classification and regression tree (CART), random forest (RF), artificial neural networks (ANN), support vector machine (SVM), Mahalanobis distance (MaD), and maximum likelihood classification (MLC)) using time-series VIIRS night light remote sensing images. On this basis, the influence of the regional differences and the importance of the multi-features were evaluated and analyzed. The results showed that (1) the RF model performed the best, with an average accuracy of 90.74%, which was much higher than the ANN, CART, SVM, MLC, and MaD. (2) The OHE targets with a lower light radiant intensity as well as a closer spatial location were the main subjects of the omission extraction, while the incorrect extractions were mostly caused by the intensive ship activities. (3) The coefficient of variation was the most important feature that affected the accuracy of the OHE target extraction, with a contribution rate of 26%. This was different from the commonly believed frequency feature in the existing research. In the context of global warming, this study can provide a valuable information reference for studies on OHE target extraction, carbon emission activity monitoring, and carbon emission dynamic assessment. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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17 pages, 11624 KiB

Open AccessArticle

Unified Framework for Ship Detection in Multi-Frequency SAR Images: A Demonstration with COSMO-SkyMed, Sentinel-1, and SAOCOM Data

by Roberto Del Prete, Maria Daniela Graziano and Alfredo Renga

Remote Sens. 2023, 15(6), 1582; https://doi.org/10.3390/rs15061582 - 14 Mar 2023

Cited by 13 | Viewed by 2513

Abstract

In the framework of maritime surveillance, vessel detection techniques based on spaceborne synthetic aperture radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. This paper deals with this topic, presenting a novel approach that exploits a [...] Read more.

In the framework of maritime surveillance, vessel detection techniques based on spaceborne synthetic aperture radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. This paper deals with this topic, presenting a novel approach that exploits a cascade application of a pre-screening algorithm and a discrimination phase. Pre-screening is based on a constant false alarm rate (CFAR) detector, whereas discrimination exploits sub-look analysis (SLA). For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images, demonstrating a significant reduction of up to 40% in false alarms within highly congested scenarios, along with a notable enhancement of the receiving operating characteristic (ROC) curves. For future synergic exploitation of multiple SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM, and COSMO-SkyMed images, is comprehensive, having images gathered over the same area with a short time lag (below 15 min). Finally, the diversified processing chains and the results for each mission product and scenario are discussed. Being the first dataset of single-look complex (SLC) SAR multi-frequency data, the present work intends to encourage additional investigation in this promising field of research. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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24 pages, 5246 KiB

Open AccessArticle

A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors

by Niyazi Arslan, Meysam Majidi Nezhad, Azim Heydari, Davide Astiaso Garcia and Georgios Sylaios

Remote Sens. 2023, 15(5), 1460; https://doi.org/10.3390/rs15051460 - 5 Mar 2023

Cited by 8 | Viewed by 3933

Abstract

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed [...] Read more.

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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18 pages, 2197 KiB

Open AccessArticle

A Multilevel Spatial and Spectral Feature Extraction Network for Marine Oil Spill Monitoring Using Airborne Hyperspectral Image

by Jian Wang, Zhongwei Li, Junfang Yang, Shanwei Liu, Jie Zhang and Shibao Li

Remote Sens. 2023, 15(5), 1302; https://doi.org/10.3390/rs15051302 - 26 Feb 2023

Cited by 7 | Viewed by 2080

Abstract

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and [...] Read more.

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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20 pages, 3298 KiB

Open AccessArticle

Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar

by Weifeng Sun, Xiaotong Li, Yonggang Ji, Yongshou Dai and Weimin Huang

Remote Sens. 2023, 15(1), 138; https://doi.org/10.3390/rs15010138 - 26 Dec 2022

Cited by 6 | Viewed by 1870

Abstract

Due to high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar in moving vessel detection, false plot-to-track association often occurs during moving vessel tracking, thus leading to track fragmentation and false tracking. In order to address this problem, [...] Read more.

Due to high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar in moving vessel detection, false plot-to-track association often occurs during moving vessel tracking, thus leading to track fragmentation and false tracking. In order to address this problem, a plot quality evaluation method is proposed and applied to plot-to-track association. Firstly, the differences in spatial correlation of echo spectrum amplitudes and position among moving vessels, clutters, and noise on a range-Doppler map are analyzed, and a plot quality index integrating multi-directional gradient, local variance, and plot position probability is developed. Then, the plots labeled as low quality are removed to reduce both the negative impact of false alarms on plot-to-track association and the computational burden. Eventually, both plot quality index and kinematic parameters are used to calculate the association cost and determine the plot-track pairs during the plot-to-track association procedure. Experimental results with field data demonstrate that the proposed plot quality index can effectively distinguish moving vessel and other plots. Compared with both the nearest neighbor data association method and the joint probability data association method, the association accuracy of the proposed method is greatly improved and, thus, the tracking continuity is enhanced in dense clutter scenarios. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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20 pages, 6087 KiB

Open AccessArticle

E-MPSPNet: Ice–Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision

by Wei Song, Hongtao Li, Qi He, Guoping Gao and Antonio Liotta

Remote Sens. 2022, 14(22), 5753; https://doi.org/10.3390/rs14225753 - 14 Nov 2022

Cited by 5 | Viewed by 1954

Abstract

Distinguishing sea ice and water is crucial for safe navigation and carrying out offshore activities in ice zones. However, due to the complexity and dynamics of the ice–water boundary, it is difficult for many deep learning-based segmentation algorithms to achieve accurate ice–water segmentation [...] Read more.

Distinguishing sea ice and water is crucial for safe navigation and carrying out offshore activities in ice zones. However, due to the complexity and dynamics of the ice–water boundary, it is difficult for many deep learning-based segmentation algorithms to achieve accurate ice–water segmentation in synthetic aperture radar (SAR) images. In this paper, we propose an ice–water SAR segmentation network, E-MPSPNet, which can provide effective ice–water segmentation by fusing semantic features and edge information. The E-MPSPNet introduces a multi-scale attention mechanism to better fuse the ice–water semantic features and designs an edge supervision module (ESM) to learn ice–water edge features. The ESM not only provides ice–water edge prediction but also imposes constraints on the semantic feature extraction to better express the edge information. We also design a loss function that focuses on both ice–water edges and semantic segmentations of ice and water for overall network optimization. With the AI4Arctic/ASIP Sea Ice Dataset as the benchmark, experimental results show our E-MPSPNet achieves the best performance compared with other commonly used segmentation models, reaching 94.2% for accuracy, 93.0% for F-score, and 89.2% for MIoU. Moreover, our E-MPSPNet shows a relatively smaller model size and faster processing speed. The application of the E-MPSPNet for processing a SAR scene demonstrates its potential for operational use in drawing near real-time navigation charts of sea ice. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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16 pages, 5269 KiB

Open AccessArticle

Shipborne HFSWR Target Detection in Clutter Regions Based on Multi-Frame TFI Correlation

by Tao Wang, Ling Zhang and Gangsheng Li

Remote Sens. 2022, 14(17), 4192; https://doi.org/10.3390/rs14174192 - 25 Aug 2022

Cited by 4 | Viewed by 2706

Abstract

High-frequency surface wave radar (HFSWR) is an important marine monitoring technology, and this new regime of radar plays an important role in large-scale, continuous early-warning monitoring at sea. In particular, shipborne HFSWR has wider applications in detecting interesting sea areas, with the advantages [...] Read more.

High-frequency surface wave radar (HFSWR) is an important marine monitoring technology, and this new regime of radar plays an important role in large-scale, continuous early-warning monitoring at sea. In particular, shipborne HFSWR has wider applications in detecting interesting sea areas, with the advantages of flexible deployment and extended detection capability. Due to the large amount of sea clutter accompanying the echo signals of shipborne HFSWR and the spread of sea clutter due to platform motion, the detection of targets in clutter regions is extremely difficult. In this paper, a multi-frame time-frequency (TF) analysis–based target-detection method is proposed. First, the sea clutter spreading area in the HFSWR echo signal is modeled, and the effects of platform motion and currents on the sea clutter spread are analyzed to determine the sea clutter coverage area; this paper focuses on frequency modeling. Then the TF image (TFI) of each range cell is obtained by TF analysis of the cells within a certain range of the echo signal, and the range cells of possible target points are determined by binary classification of the TFI through a convolutional neural network. Finally, the location of the final target point is obtained by correlation of multi-frame TFIs. Shipborne HFSWR field experiments show that the proposed detection method performs well in detecting targets concealed by sea clutter. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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21 pages, 6711 KiB

Open AccessArticle

Influence of Radar Parameters and Sea State on Wind Wave-Induced Velocity in C-Band ATI SAR Ocean Surface Currents

by Rui Zhang, Jie Zhang, Xi Zhang, Chenghui Cao, Xiaochen Wang, Gui Gao, Genwang Liu and Meng Bao

Remote Sens. 2022, 14(17), 4135; https://doi.org/10.3390/rs14174135 - 23 Aug 2022

Cited by 3 | Viewed by 1962

Abstract

Wind wave-induced artifact surface velocity (WASV) is an important component of the sea surface motions detected by synthetic aperture radar (SAR) systems. Understanding the characteristics of the interference of WASV on SAR current velocity estimates is necessary to improve the accuracy of retrievals. [...] Read more.

Wind wave-induced artifact surface velocity (WASV) is an important component of the sea surface motions detected by synthetic aperture radar (SAR) systems. Understanding the characteristics of the interference of WASV on SAR current velocity estimates is necessary to improve the accuracy of retrievals. In this study, we assessed and analyzed the sensitivity of WASV in C-band along-track interferometric (ATI) SAR to radar configuration, wind field, swell field, and a wave spectrum model. Results showed that the influence of wind speed on WASV increased with the current velocity. The swell also affected WASV, especially at higher wind speeds; WASV was more strongly influenced by swell amplitude than by swell wavelength. In terms of radar configurations, results showed that VV polarization was more suitable than HH polarization in the estimation of WASV. The interference of WASV was minimal at moderate incidence angles (around 40°), and an appropriate ATI baseline selection was also given. The WASV was more strongly influenced by sea states than by the wave spectrum model or by a spreading function. The findings of this study improve our understanding of WASV and provide a reference for the design of future ATI SAR current measurement instruments and projects. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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15 pages, 3286 KiB

Open AccessArticle

A Joint Method for Wave and Wind Field Parameter Inversion Combining SAR with Wave Spectrometer Data

by Yong Wan, Xiaona Zhang, Chenqing Fan, Ruozhao Qu and Ennan Ma

Remote Sens. 2022, 14(15), 3601; https://doi.org/10.3390/rs14153601 - 27 Jul 2022

Cited by 6 | Viewed by 1782

Abstract

Synthetic aperture radar (SAR) and wave spectrometer are common methods for observing the ocean wind field and waves on the sea surface, but both have limitations. Due to the influence of velocity bunching modulation, SAR wave observation is limited by the azimuth cut-off [...] Read more.

Synthetic aperture radar (SAR) and wave spectrometer are common methods for observing the ocean wind field and waves on the sea surface, but both have limitations. Due to the influence of velocity bunching modulation, SAR wave observation is limited by the azimuth cut-off phenomenon. Meanwhile, SAR relies on an external wind direction source, so it is difficult for SAR to observe wind fields independently. There is no azimuthal cut-off phenomenon when the spectrometer observes the sea surface, but its azimuth resolution is much lower than SAR. Combining the above characteristics, the joint inversion of SAR and wave spectrometer for sea-state parameters becomes possible. In this paper, a joint method for wind field and wave parameter inversion combining SAR with spectrometer data is proposed. In this method for wave parameter inversion, the wave spectrum of the spectrometer was used as a first-guess spectrum of SAR wave spectrum inversion, the fit wave spectrum obtained by joint inversion and the modified wave spectrum of the spectrometer were fused to form a new spectrum, and the wave parameters were calculated. For wind field parameter inversion, wind direction was obtained using a wave spectrometer, and was used as the input of the SAR wind field inversion. Wind speed was obtained using the CMOD5.N method. Collocated data from Sentinel-1 SAR, the wave spectrometer, the National Data Buoy Center (NDBC) buoy, and the European Centre for Medium-Range Weather Forecasting (ECMWF) are used to verify the proposed method. Sea parameters retrieved from the spectrometer and SAR are compared to the buoy and ECMWF data. The results show that the root mean square errors of significant wave height, mean wave period, wind direction and wind speed are 0.37 m, 1.02 s, 22.7° and 1.06 m/s with ECMWF data, and 0.35 m, 0.78 s, 18.22° and 0.92 m/s with buoy data, respectively. By comparing the inversion results with the L2 products of SAR and SWIM, it can be concluded that the inversion accuracy of the joint method is higher in the middle and low sea conditions. Therefore, the joint inversion method for wind field and wave parameters proposed in this paper has good results, which verifies the accuracy of the joint inversion method. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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25 pages, 1200 KiB

Open AccessArticle

A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images

by Yanbo Wei, Yalin Liu, Yifei Lei, Ruiyao Lian, Zhizhong Lu and Lei Sun

Remote Sens. 2022, 14(15), 3600; https://doi.org/10.3390/rs14153600 - 27 Jul 2022

Cited by 4 | Viewed by 1893

Abstract

To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image [...] Read more.

To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall. However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV). Then, an unsupervised K-means clustering learning method is used to obtain the clustering centers. Based on the constituted CCFV and the K-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by

10.0 %

,

6.3 %

,

2.0 %

and

0.6 %

, respectively, for the proportion of the

25 %

training dataset. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing - Part 2)

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