Статті в журналах: "Marine radar images"

1

Austin, G. L., A. Bellon, M. Riley, and E. Ballantyne. "Navigation by Computer Processing of Marine Radar Images." Journal of Navigation 38, no. 3 (September 1985): 375–83. http://dx.doi.org/10.1017/s0373463300032744.

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The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.

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2

Wei, Yanbo, Yalin Liu, Yifei Lei, Ruiyao Lian, Zhizhong Lu, and Lei Sun. "A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images." Remote Sensing 14, no. 15 (July 27, 2022): 3600. http://dx.doi.org/10.3390/rs14153600.

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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.

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3

Mingozzi, Matteo, Francesca Salvioli, and Francesco Serafino. "X-Band Radar for Cetacean Detection (Focus on Tursiops truncatus) and Preliminary Analysis of Their Behavior." Remote Sensing 12, no. 3 (January 25, 2020): 388. http://dx.doi.org/10.3390/rs12030388.

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Cetaceans are protected species all over the world, most of them are vulnerable, endangered, or data deficient (according to International Union for Conservation of Nature - IUCN red list). X-band radars detect the echo of the electromagnetic signal reflected by an obstacle or a ship (target). The application of X-band radar to the detection of cetaceans is a new and innovative field of research that could improve the automation of marine mammal data collection, and this is the first time in the Mediterranean Sea. The aim of this work was to test the capability of X-band radar installed along the coast (ground-based) to detect and track cetaceans in a range of approximately 2.5 nautical miles from the radar antenna. Data collection included a part of field work, implemented through the acquisition of photographic images and target’s radar detection (by the panoramic terrace Santa Maria in Corniglia), and a part, performed in the laboratory, of data analysis. The work was undertaken between May and November 2018. During this period, 30 days of monitoring were carried out (about 300 h) and about 10,000 radar images were recorded. The first results showed that we were able to recognize the target “cetacean” from the other common targets (boats, buoys, etc.) detected by the radar. In particular 70 dolphins were sighted by visual census; 12 of them were recognized on radar images. Radar images allowed extraction of dolphin dive time (between 2 and 15 s). The next step will be to allow the radar to identify the presence of marine mammals itself since it also works at night and with low visibility. This technique could complement the protection measures of cetaceans, highlighting their presence at sea even if it is impossible with waves higher than 0.8 m and over distances greater than 2.5 km.

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4

Wang, Hui, Haiyang Qiu, Pengfei Zhi, Lei Wang, Wei Chen, Rizwan Akhtar, and Muhammad Asif Zahoor Raja. "Study of Algorithms for Wind Direction Retrieval from X-Band Marine Radar Images." Electronics 8, no. 7 (July 8, 2019): 764. http://dx.doi.org/10.3390/electronics8070764.

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After decades of research, X-band marine radars have been broadly used for wind measurement. For retrieving the wind direction based on the wind-induced streaks, a lot of effort has been expended on three celebrated approaches—the local gradient method (LGM), the adaptive reduced method (ARM), and the energy spectrum method (ESM). This paper presents a scientific study of these methods. The contrast of retrieving the real measured marine radar images and vane measured results is evaluated, in perspective of the error statistics and algorithm operation efficiency. Interference factors, such as the historical information of the measured area, reference wind speed, and sea condition showing in the monitoring equipment are also concerned. The tentative results showed that LGM is robust, which can be implemented in most radar images, because it allows for a lower selection of requirements compared with the other two methods. For ARM, the better retrieval performance is a tradeoff with extra computation, which is expensive. ESM is superior to the other two algorithms in terms of accuracy and computation load; however, this algorithm is sensitive in rain-contaminated radar images, meaning it is a good choice for data post-processing in the lab.

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5

Guo, Muzhuang, Chen Guo, Chuang Zhang, Daheng Zhang, and Zongjiang Gao. "Fusion of Ship Perceptual Information for Electronic Navigational Chart and Radar Images based on Deep Learning." Journal of Navigation 73, no. 1 (June 14, 2019): 192–211. http://dx.doi.org/10.1017/s0373463319000481.

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Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.

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6

Chen, Zhongbiao, Yijun He, and Wankang Yang. "Study of Ocean Waves Measured by Collocated HH and VV Polarized X-Band Marine Radars." International Journal of Antennas and Propagation 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8257930.

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The significant wave height (SWH) retrieved from collocated HH and VV polarized X-band marine radars under different sea states is studied. The SWH are retrieved from different principal components of X-band marine radar image sequence. As compared with the SWH measured by a buoy, the root-mean-square errors of the SWH are 0.32–0.45 m for VV polarization, and they are 0.37–0.60 m for HH polarization. At the wind speeds of 0–5 m/s, the SWH can be derived from VV polarized radar images, while the backscatter of HH polarized radar is too weak to contain wave signals at very low wind speeds (~0–3 m/s). At the wind speeds of 5–18 m/s, the SWH retrieved from VV polarization coincide well with the SWH measured by the buoy, while the SWH retrieved from HH polarization correspond with the changes of the wind speed. At the wind speeds of 18–26 m/s, the influence of wave breaking on HH polarization is more important than that on VV polarization. This indicates that the imaging mechanisms of HH polarized X-band marine radar are different from those of VV polarized X-band marine radar.

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7

Wu, Chao, Qing Wu, Feng Ma, and Shuwu Wang. "A novel positioning approach for an intelligent vessel based on an improved simultaneous localization and mapping algorithm and marine radar." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 233, no. 3 (July 11, 2018): 779–92. http://dx.doi.org/10.1177/1475090218784449.

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This research proposes a simultaneous localization and mapping approach to obtain the positioning information of a vessel in accordance with sequential radar images. At the very beginning, the digital image preprocessing methods are used to obtain the static feature point in radar images. Subsequently, the trajectory of the vessel is calculated based on a simultaneous localization and mapping–based algorithm. Finally, the calculated vessel trajectory is compared with the actual trajectory to verify the validity of the proposed approach. With the help of this approach, marine radar is capable of providing temporal positioning information of the vessel from a plethora of blips captured in frame-by-frame radar images. The proposed approach is unique in that it used marine radar as the only sensor to obtain the positioning information of the vessel. Particularly, field testing has been conducted to validate the effectiveness and accuracy of the proposed approach.

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8

Ji, Xing, Jia Yuan Zhuang, and Yu Min Su. "Marine Radar Target Detection for USV." Advanced Materials Research 1006-1007 (August 2014): 863–69. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.863.

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Unmanned surface vehicles (USV) have become an intense research area because of their extensive applications. Marine radar is the most important environmental perception sensor for USV. Aiming at the problems of noise jamming, uneven brightness, target lost in marine radar images, and the high-speed USV to the requirement of real-time and reliability, this paper proposes the radar image target detection algorithms which suitable for embedded marine radar target detection system. The smoothing algorithm can adaptive select filter in noise, border and background areas, improves the efficiency and smoothing effect. Based on the iterative threshold, the tolerance coefficient is selected by the histogram, ensures the robust of segmentation algorithm. The location, area and invariant moments features can be extracted from the radar image which after connected-component labeling. The actual radar image processing results demonstrate the effectiveness of the proposed algorithms.

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9

Guerrero, José Miguel, Andreas Muñoz, Matilde Santos, and Gonzalo Pajares. "A new Concentric Circles Detection method for Object Detection applied to Radar Images." Journal of Navigation 72, no. 04 (February 27, 2019): 1070–88. http://dx.doi.org/10.1017/s0373463318001169.

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In this work, a new concentric circles detection method for object detection is proposed. It has been applied to the images of a commercial radar, captured with a Charge-Coupled Device (CCD) camera. The processing includes the detection of centres and concentric circles in the images and the identification of the radar scale. Several methods found in the literature have been applied and compared with our novel proposal for multiple concentric circles detection, called “Propagation Method based on Circular Regression”. This methodology has been validated with real radar images, proving its efficiency in obtaining the distance of any object to a marine vessel, with high accuracy and low computational cost, in real time. This system can not only be applied to most existing radars in the market by adjusting the parameters of each model but our proposal for concentric circle detection can be also applied to other sensing applications.

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10

Xu, Jin, Haixia Wang, Can Cui, Peng Liu, Yang Zhao, and Bo Li. "Oil Spill Segmentation in Ship-Borne Radar Images with an Improved Active Contour Model." Remote Sensing 11, no. 14 (July 18, 2019): 1698. http://dx.doi.org/10.3390/rs11141698.

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Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film detection in ship-borne radar images using pixel area threshold parameters. After applying a pre-processing scheme with a Laplace operator, an Otsu threshold, and mean and median filtering, the shape and area of the oil film can be calculated rapidly. Compared with other ACMs, the improved Local Binary Fitting (LBF) model is robust and has a fast calculation speed for uniform ship-borne radar sea clutter images. The proposed method achieves better results and higher operation efficiency than other automatic and semi-automatic methods for oil film detection in ship-borne radar images. Furthermore, it provides a scientific basis to assess pollution scope and estimate the necessary cleaning materials during oil spills.

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11

Lu, Zhizhong, Lei Sun, and Ying Zhou. "A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images." Applied Sciences 11, no. 4 (February 9, 2021): 1565. http://dx.doi.org/10.3390/app11041565.

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Currently, it is a hot research topic to retrieve the wave parameters by using X-band marine radar. However, the rainfall noise usually exists in the collected marine radar images, which seriously interferes with the extraction of the wave parameters. To reduce the influence of rainfall noise, the zero-pixel percentage (ZPP) method is widely used to detect rainfall in radar images, but the detection accuracy is limited, and the selection of the threshold needs to be further studied. Based on the ZPP method, the ratio of zero intensity to echo (RZE) method for rainfall detection is proposed in this paper. The detection threshold is determined by statistical analysis of a large amount of radar data. Additionally, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. In addition, the concept of the occlusion area is proposed. The proposed area and the wave area are used as the rainfall detection area of the radar image, respectively, for experimental research. The data obtained from the Pingtan experimental base in Fujian Province are used to verify the effectiveness of the proposed method. The experimental results show that the detection accuracy of the proposed method is 11.7% higher than that of the ZPP method, and the accuracy of rainfall intensity level retrieval is 84%.

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12

Nieto Borge, JoséC, Germán RodrÍguez RodrÍguez, Katrin Hessner, and Paloma Izquierdo González. "Inversion of Marine Radar Images for Surface Wave Analysis." Journal of Atmospheric and Oceanic Technology 21, no. 8 (August 2004): 1291–300. http://dx.doi.org/10.1175/1520-0426(2004)021<1291:iomrif>2.0.co;2.

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13

Zhang, Chuang, Meihan Fang, Chunyu Yang, Renhai Yu, and Tieshan Li. "Perceptual Fusion of Electronic Chart and Marine Radar Image." Journal of Marine Science and Engineering 9, no. 11 (November 10, 2021): 1245. http://dx.doi.org/10.3390/jmse9111245.

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Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.

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14

Chen, Xinwei, and Weimin Huang. "Texture Features and Unsupervised Learning-Incorporated Rain-Contaminated Region Identification From X-Band Marine Radar Images." Marine Technology Society Journal 54, no. 4 (July 1, 2020): 59–67. http://dx.doi.org/10.4031/mtsj.54.4.7.

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AbstractA novel method is proposed for identifying rain-contaminated regions in X-band marine radar images. Due to the difference of texture between rain-contaminated and rain-free echoes, a Gabor filter bank and discrete wavelet transform (DWT) are introduced to filter marine radar images and generate texture features. Feature vectors extracted from each pixel of the training samples are input into a clustering model, which is trained using unsupervised learning techniques such as k-means and a self-organizing map (SOM). After distinguishing between rain-free and rain-contaminated clusters, the proposed method is able to cluster pixels into rain-free and rain-contaminated types automatically. Images collected from a shipborne marine radar in a sea trial off the east coast of Canada under rain conditions are utilized to validate the proposed method. Identification results obtained from several clustering models with different combinations of cluster number, texture features, and clustering methods show that rain-contaminated pixels are effectively detected, with an overall identification accuracy of 89.1% for both k-means‐based (k = 4) and 2 × 2-neuron SOM-based clustering models.

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15

Mityagina, M. I. "Intensity of convective motions in marine atmospheric boundary layer retrieved from ocean surface radar imagery." Nonlinear Processes in Geophysics 13, no. 3 (July 24, 2006): 303–8. http://dx.doi.org/10.5194/npg-13-303-2006.

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Abstract. The paper focuses on the occurrence and development of coherent structures observed in the atmosphere above ocean under natural conditions. Microwave imaging radars are suggested as data take instruments. The phenomena of marine atmospheric cells and rolls onset, horizontal planform, aspect ratio and scaling phenomena are examined. Convective patterns manifested in radar images and information derived on the intensity of atmospheric motion are discussed.

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16

Chen, Zhongbiao, Biao Zhang, Vladimir Kudryavtsev, Yijun He, and Xiaoqing Chu. "Estimation of Sea Surface Current from X-Band Marine Radar Images by Cross-Spectrum Analysis." Remote Sensing 11, no. 9 (April 30, 2019): 1031. http://dx.doi.org/10.3390/rs11091031.

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The cross-spectral correlation approach has been used to estimate the wave spectrum from optical and radar images. This work aims to improve the cross-spectral approach to derive current velocity from the X-band marine radar image sequence, and evaluate the application conditions of the method. To reduce the dependency of gray levels on range and azimuth, radar images are preprocessed by the contrast-limited adaptive histogram equalization. Two-dimensional cross-spectral coherence and phase are derived from neighboring X-band marine radar images, and the phases with large coherences are used to estimate the phase velocity and angular frequency of waves, which are first fitted with the theoretical dispersion relation by different least square models, and then the current velocity can be determined. Compared with the current velocities measured by a current meter, the root-mean-square error, correlation coefficient, bias, and relative error are 0.15 m/s. 0.88, –0.05 m/s, and 7.79% for the north-south velocity, and 0.14 m/s, 0.86, 0.06 m/s, and 10.75% for the east-west velocity in the experimental area, respectively. The preprocessing, critical coherence, and the number of images for applying the cross-spectral approach, are discussed.

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17

Austin, G. L., A. Bellon, and E. Ballantyne. "Sea Trials of a Navigation System Based on Computer Processing of Marine Radar Images." Journal of Navigation 40, no. 1 (January 1987): 73–80. http://dx.doi.org/10.1017/s037346330000031x.

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A system which yields the automatic positioning of a ship from computer analysis of marine radar images of nearby coastlines has been tested on data from the survey vessel Maxwell while proceeding in and out of Halifax harbour. Differences between radar-determined position fixes and those obtained by a microwave navigation system with an error of the order i o m show little evidence of additional error when the origin of the radar images used as ‘reference map’ is within 500 m of the actual position. As the distance increases the accuracy slowly decreases.

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18

Zinchenko, Victoria, Leonid Vasilyev, Svein Olav Halstensen, and Yuming Liu. "An improved algorithm for phase-resolved sea surface reconstruction from X-band marine radar images." Journal of Ocean Engineering and Marine Energy 7, no. 1 (February 2021): 97–114. http://dx.doi.org/10.1007/s40722-021-00189-9.

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AbstractWe present a modified methodology for phase-resolved surface wave reconstruction from incoherent X-band marine radar images. The method is based on the linear wave theory and uses the linear dispersion relation to extract the valuable signals associated with gravity waves. A parameter optimization of the proposed modification is performed based on simulated synthetic radar images. The quantitative comparisons in the accuracy of the standard and modified reconstruction methods are made for both simulated and real radar images. The correlation coefficient between reconstructed and true wave elevations is improved up to 0.9–0.92 for the present modified method from 0.69 to 0.74 for the standard method for the simulated sea surfaces. The wave spectra reconstructed from the real X-band radar measurements are in good agreement with those obtained from the independent point measurement by Miros RangeFinder for both unimodal and bimodal seas.

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19

Støle-Hentschel, Susanne, Jörg Seemann, José Carlos Nieto Borge, and Karsten Trulsen. "Consistency between Sea Surface Reconstructions from Nautical X-Band Radar Doppler and Amplitude Measurements." Journal of Atmospheric and Oceanic Technology 35, no. 6 (June 2018): 1201–20. http://dx.doi.org/10.1175/jtech-d-17-0145.1.

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AbstractThis study comprises the analysis and the interpretation of the coherent and the noncoherent parts of a coherent-on-receive microwave radar at grazing incidence conditions. The Doppler measurement is an extension of standard civil marine radar technology. While intensity images require interpretation based on understanding the underlying imaging mechanism, the Doppler signal measures the motion of an area of sea surface and is therefore closely related to the wave physics. Both the measured Doppler signal and the backscatter intensity signal are suitable for surface inversion and give almost identical surface elevations. A statistical comparison with a nearby buoy showed good correlation for the significant wave height and the peak period. By comparing the Doppler signal and the amplitude in the backscatter, the study amends the understanding of imaging mechanisms in marine radars at grazing incidence.

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20

Yu, Huanyu, Zhizhong Lu, and Hui Wang. "Wind Direction Extraction from X-Band Marine Radar Images Based on the Attenuation Horizontal Component." Remote Sensing 15, no. 16 (August 10, 2023): 3959. http://dx.doi.org/10.3390/rs15163959.

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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.

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Yu, Huanyu, Hui Wang, and Zhizhong Lu. "Wind-Direction Estimation from Single X-Band Marine Radar Image Improvement by Utilizing the DWT and Azimuth-Scale Expansion Method." Entropy 24, no. 6 (May 24, 2022): 747. http://dx.doi.org/10.3390/e24060747.

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In this study, a method based on the discrete wavelet transform (DWT) and azimuth-scale expansion is presented to retrieve the sea-surface wind direction from a single X-band marine radar image. The algorithm first distinguishes rain-free and rain-contaminated radar images based on the occlusion zero-pixel percentage and then discards the rain-contaminated images. The radar image whose occlusion areas have been removed is decomposed into different low-frequency sub-images by the 2D DWT, and the appropriate low-frequency sub-image is selected. Images collected with a standard marine HH-polarized X-band radar operating at grazing incidence display a single intensity peak in the upwind direction. To overcome the influence of the occlusion area, before determining the wind direction, the data near the ship bow are shifted to expand the azimuth scale of the data. Finally, a harmonic function is least-square-fitted to the range-averaged radar return of the low-frequency sub-image as a function of the antenna look azimuth to determine the wind direction. Different from the wind-direction retrieval algorithms previously presented, this method is more suitable for sailing ships, as it functions well even if the radar data are heavily blocked. The results show that compared with the single-curve fitting algorithm, the algorithm based on DWT and azimuth-scale expansion can improve the wind-direction results in sailing ships, showing a reduction of 7.84° in the root-mean-square error with respect to the reference.

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Chang, Ruili. "Development and application of data mining method for synthetic aperture radar image ship inspection based on big data application technology." Journal of Physics: Conference Series 2294, no. 1 (June 1, 2022): 012006. http://dx.doi.org/10.1088/1742-6596/2294/1/012006.

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Abstract Synthetic aperture radar belongs to the radar signal working in microwave band, which has the characteristics of strong penetrating performance, large area imaging, all-weather and so on, and is widely used in the civil field and military field. Especially in the background of the information era, synthetic aperture radar imaging technology has developed to different degrees, and the resolution of synthetic aperture radar images has been significantly improved, which is highly valued by the target detection field, and the detection of naval targets through the reasonable use of synthetic aperture radar images has become an important application direction in the field of marine remote sensing. Based on this, this paper analyzes the characteristics of ship targets in SAR images, analyzes the differences between them and optical images from different aspects, and then concentrates on the most common statistical models and prediction methods of clutter analysis in SAR images, proposes the most reasonable way of clutter distribution simulation, and then uses the experimental way to accurately evaluate the complex sea clutter in SAR images, so as to propose the similar characteristics of the ship detection method‥

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23

Chen, Weishi. "Interactive processing of radar target detection and tracking." Aircraft Engineering and Aerospace Technology 90, no. 9 (November 14, 2018): 1337–45. http://dx.doi.org/10.1108/aeat-07-2016-0115.

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Purpose An interactive processing scheme is proposed to improve the target detection probability as well as the tracking performance of the radar system. Design/methodology/approach Firstly, with the spatial-correlated features extracted from the foreground and background statistical models, the thresholds were adapted to distinguish the dim small targets from clutters in the complex incoherent radar images. Then, the target trajectories were constructed with the target tracking algorithm. According to the temporal correlation with the target life cycle, the thresholding values were modified in the neighbourhood of the predicted positions to improve the detection sensitivity in these areas during the tracking process. Finally, the temporal-correlated features of the remained clutters were used to further reduce the false alarm rate. Findings The proposed algorithm was applied on the simulated data, as well as the image sequences obtained with the incoherent marine radars. The detection results demonstrated that the interactive algorithm could detect and track the dim small targets with relatively low false alarm rate. Practical implications The interactive processing scheme could be applied for low-altitude airspace surveillance with incoherent marine radar. Originality/value The proposed scheme outperforms the classical radar target detection algorithms and the state-of-the-art image processing algorithms for video-based surveillance.

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Chen, Xiaolong, Jian Guan, Xiaoqian Mu, Zhigao Wang, Ningbo Liu, and Guoqing Wang. "Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet." Remote Sensing 13, no. 19 (September 26, 2021): 3856. http://dx.doi.org/10.3390/rs13193856.

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Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

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25

Liu, Peng, Yancheng Zhao, Bingxin Liu, Ying Li, and Peng Chen. "Oil spill extraction from X-band marine radar images by power fitting of radar echoes." Remote Sensing Letters 12, no. 4 (February 28, 2021): 345–52. http://dx.doi.org/10.1080/2150704x.2021.1892852.

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26

Ludeno and Serafino. "Estimation of the Significant Wave Height from Marine Radar Images without External Reference." Journal of Marine Science and Engineering 7, no. 12 (November 27, 2019): 432. http://dx.doi.org/10.3390/jmse7120432.

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In the context of the sea state monitoring by means of the X-band marine radar, the estimation of a significant wave height (Hs) is, currently, one of the most challenging tasks. For its estimation, a calibration is usually required using an external reference, such as in situ sensors, and mainly buoys. In this paper, a method that allows us to avoid the need for an external reference for Hs estimation is presented. This strategy is, mainly, based on the correlation between a raw radar image and the corresponding non-calibrated wave elevation image to which varying its amplitude by using a scale factor creates a mathematical model for the radar imaging. The proposed strategy has been validated by considering a simulated waves field, generated at varying sea state conditions. The results show a good estimation of the significant wave height, confirmed by a squared correlation coefficient greater than 0.70 for each considered sea state.

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27

S. Ashwin, J., and N. Manoharan. "Convolutional Neural Network Based Target Recognition for Marine Search." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 2 (November 1, 2017): 561. http://dx.doi.org/10.11591/ijeecs.v8.i2.pp561-563.

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<p>The key point of marine search and rescue is to find out and recognize the distress objects. At present, the visual search method is usually adopted to detect the ships in distress, and this method can only be used at good sea condition and visibility. In this paper, a new target detection and recognition system is proposed. The parameters of radar transmitter and echo graphics and the invariant moments of radar images are extracted as the system’s recognition features, and the system’s target classifier is based on Convolutional Neural Networks (CNN). The developed recognition classifier has been tested using three kinds of target Images, the target’s features are used as the inputs of trained CNN and the outputs of networks are target classification. Sea experimental results show that the proposed method is well-clustering and with high classified accuracy.</p>

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28

V. Ramachandran, Capt. "Artificial Neural Network Based Target Recognition for Marine Search." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (December 1, 2017): 616. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp616-618.

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<p>The key point of marine search and rescue is to find out and recognize the distress objects. At present, the visual search method is usually adopted to detect the ships in distress, and this method can only be used at good sea condition and visibility. In this paper, a new target detection and recognition system is proposed. The parameters of radar transmitter and echo graphics and the invariant moments of radar images are extracted as the system’s recognition features, and the system’s target classifier is based on Artificial Neural Networks (ANN). The developed recognition classifier has been tested using three kinds of target Images, the target’s features are used as the inputs of trained ANN and the outputs of networks are target classification. Sea experimental results show that the proposed method is well-clustering and with high classified accuracy.</p>

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29

Zheng, Yan, Zhen Shi, Zhizhong Lu, and Wenfeng Ma. "A Method for Detecting Rainfall From X-Band Marine Radar Images." IEEE Access 8 (2020): 19046–57. http://dx.doi.org/10.1109/access.2020.2968601.

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30

Lyzenga, David R., and David T. Walker. "A Simple Model for Marine Radar Images of the Ocean Surface." IEEE Geoscience and Remote Sensing Letters 12, no. 12 (December 2015): 2389–92. http://dx.doi.org/10.1109/lgrs.2015.2478390.

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31

Chen, Rong, Baozhu Jia, Long Ma, Jin Xu, Bo Li, and Haixia Wang. "Marine Radar Oil Spill Extraction Based on Texture Features and BP Neural Network." Journal of Marine Science and Engineering 10, no. 12 (December 5, 2022): 1904. http://dx.doi.org/10.3390/jmse10121904.

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Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m2. The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents.

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32

Chen, Xiaolong, Xiaoqian Mu, Jian Guan, Ningbo Liu, and Wei Zhou. "Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images." Frontiers of Information Technology & Electronic Engineering 23, no. 4 (April 2022): 630–43. http://dx.doi.org/10.1631/fitee.2000611.

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33

Shen, Chengxi, Weimin Huang, Eric Gill, Ruben Carrasco, and Jochen Horstmann. "An Algorithm for Surface Current Retrieval from X-band Marine Radar Images." Remote Sensing 7, no. 6 (June 11, 2015): 7753–67. http://dx.doi.org/10.3390/rs70607753.

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34

Wang, Yali, and Weimin Huang. "An Algorithm for Wind Direction Retrieval From X-Band Marine Radar Images." IEEE Geoscience and Remote Sensing Letters 13, no. 2 (February 2016): 252–56. http://dx.doi.org/10.1109/lgrs.2015.2508284.

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35

Sun, Zequn, Chunning Meng, Jierong Cheng, Zhiqing Zhang, and Shengjiang Chang. "A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images." Remote Sensing 14, no. 24 (December 13, 2022): 6312. http://dx.doi.org/10.3390/rs14246312.

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In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.

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36

Simpson, Alexandra, Merrick Haller, David Walker, Patrick Lynett, and David Honegger. "Wave-by-Wave Forecasting via Assimilation of Marine Radar Data." Journal of Atmospheric and Oceanic Technology 37, no. 7 (July 1, 2020): 1269–88. http://dx.doi.org/10.1175/jtech-d-19-0127.1.

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AbstractThis work describes a phase-resolving wave-forecasting algorithm that is based on the assimilation of marine radar image time series. The algorithm is tested against synthetic data and field observations. The algorithm couples X-band marine radar observations with a phase-resolving wave model that uses the linear mild slope equations for reconstruction of water surface elevations over a large domain of O(km) and a prescribed time window of O(min). The reconstruction also enables wave-by-wave forecasting through forward propagation in space and time. Marine radar image time series provide the input wave observations through a previously given relationship between backscatter intensity and the radial component of the sea surface slope. The algorithm assimilates the wave slope information into the model via a best-fit wave source function at the boundary that minimizes the slope reconstruction error over an annular region at the outer ranges of the radar images. The wave model is then able to propagate the waves across a polar domain to a location of interest at nearer ranges. The constraints on the method for achieving real-time forecasting are identified, and the algorithm is verified against synthetic data and tested using field observations.

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37

Guliyev, A. Sh, and T. A. Khlebnikova. "Methods of joint processing of complex radar interferograms and multispectral optical images under temporal decorrelation." Interexpo GEO-Siberia 4 (May 18, 2022): 3–9. http://dx.doi.org/10.33764/2618-981x-2022-4-3-9.

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This article substantiates the relevance of the study, the characteristics of the joint use of optical multispectral survey, radar interferometry and partial polarimetry, identifies the scope of interpretation of the radar-optical composite built by optical and radar data, and provides a mathematical model for image processing of the water surface area. The quantitative assessment of these automated or semi-automated methods is not inferior to the accuracy of traditional methods for assessing the state of the offshore marine environment. It was shown that the most efficient approach is the direct use of the ResNet-10 deep learning algorithm on scenes when combined with complex (amplitude and phase) centimeter-range radar images and multispectral optical images of Sentinel platforms. This approach made it possible to detect 86.72% of all spots in the scenes and had an average accuracy of 75.35%. The approach has also showed a significantly reduced ability to detect patches when the local wind speed was below 2 m/s or above 12 m/s.

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38

Zhang, Yi, Chengyi Wang, Jingbo Chen, and Futao Wang. "Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery." Remote Sensing 14, no. 5 (March 3, 2022): 1249. http://dx.doi.org/10.3390/rs14051249.

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Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool for stable monitoring of marine raft aquaculture areas since it is all-weather, all-day, and cloud-penetrating. However, the scattering signal of marine raft aquaculture areas is affected by speckle noise and sea state, so their features in SAR images are complex. Thus, it is challenging to extract marine raft aquaculture areas from SAR images. In this paper, we propose a method to extract marine raft aquaculture areas from Sentinel-1 images based on the analysis of the features for marine raft aquaculture areas. First, the data are preprocessed using multitemporal phase synthesis to weaken the noise interference, enhance the signal of marine raft aquaculture areas, and improve the significance of the characteristics of raft aquaculture areas. Second, the geometric features of the marine raft aquaculture area are combined to design the model structure and introduce the shape constraint module, which adds a priori knowledge to guide the model convergence direction during the training process. Experiments verify that the method outperforms the popular semantic segmentation model with an F1 of 84.52%.

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39

Vicen-Bueno, Raul, Jochen Horstmann, Eric Terril, Tony de Paolo, and Jens Dannenberg. "Real-Time Ocean Wind Vector Retrieval from Marine Radar Image Sequences Acquired at Grazing Angle." Journal of Atmospheric and Oceanic Technology 30, no. 1 (January 1, 2013): 127–39. http://dx.doi.org/10.1175/jtech-d-12-00027.1.

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Abstract This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatter on wind direction and speed. This algorithm retrieves the wind vector using radar measurements in the range of 200–1500 m. Wind directions are retrieved from radar images integrated over time and smoothed (averaged) in space by searching for the maximum radar cross section in azimuth as the radar cross section is largest for upwind directions. Wind speeds are retrieved by an empirical third-order polynomial geophysical model function (GMF), which depends on the range distance in the upwind direction to a preselected intensity level and the intensity level. This GMF is approximated from a dataset of collocated in situ wind speed and radar measurements (~31 000 measurements, ~56 h). The algorithm is validated utilizing wind and radar measurements acquired on the Research Platform (R/P) FLIP (for Floating Instrumentation Platform) during the 13-day Office of Naval Research experiment on High-Resolution Air–Sea Interaction (HiRes) in June 2010. Wind speeds ranged from 4 to 22 m s−1. Once the proposed algorithm is tuned, standard deviations and biases of 14° and −1° for wind directions and of 0.8 and −0.1 m s−1 for wind speeds are observed, respectively. Additional studies of uncertainty and error of the retrieved wind speed are also reported.

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40

Wang, Hui, Shiyu Li, Haiyang Qiu, Zhizhong Lu, Yanbo Wei, Zhiyu Zhu, and Huilin Ge. "Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images." Remote Sensing 15, no. 8 (April 14, 2023): 2078. http://dx.doi.org/10.3390/rs15082078.

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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.

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41

Liu, Peng, Ying Li, Bingxin Liu, Peng Chen, and and Jin Xu. "Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding." Remote Sensing 11, no. 7 (March 28, 2019): 756. http://dx.doi.org/10.3390/rs11070756.

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Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

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42

Lehner, Susanne, Andrey Pleskachevsky, Domenico Velotto, and Sven Jacobsen. "Meteo-Marine Parameters and Their Variability Observed by High Resolution Satellite Radar Images." Oceanography 26, no. 2 (June 1, 2013): 80–91. http://dx.doi.org/10.5670/oceanog.2013.36.

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43

Wei, Yanbo, Yan Zheng, and Zhizhong Lu. "A Method for Retrieving Wave Parameters From Synthetic X-Band Marine Radar Images." IEEE Access 8 (2020): 204880–90. http://dx.doi.org/10.1109/access.2020.3037157.

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44

Gommenginger, C. P., N. P. Ward, G. J. Fisher, I. S. Robinson, and S. R. Boxall. "Quantitative Microwave Backscatter Measurements from the Ocean Surface Using Digital Marine Radar Images." Journal of Atmospheric and Oceanic Technology 17, no. 5 (May 2000): 665–78. http://dx.doi.org/10.1175/1520-0426(2000)017<0665:qmbmft>2.0.co;2.

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45

Chen, Zhongbiao, Yijun He, Biao Zhang, and Zhongfeng Qiu. "Determination of nearshore sea surface wind vector from marine X-band radar images." Ocean Engineering 96 (March 2015): 79–85. http://dx.doi.org/10.1016/j.oceaneng.2014.12.019.

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46

Ivanov, Y. Yu. "ASSESSMENT OF MARINE OIL POLLUTION USING KOSMOS-1870 AND ALMAZ-1 RADAR IMAGES." Mapping Sciences and Remote Sensing 35, no. 3 (July 1998): 202–17. http://dx.doi.org/10.1080/07493878.1998.10642092.

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47

Ivanov, A. Yu, D. V. Khlebnikov, B. V. Konovalov, S. K. Klimenko, and N. V. Terleeva. "Kerch Strait and Its Technogenic Pollution: Possibilities of Optical and Radar Remote Sensing." Ecology and Industry of Russia 25, no. 8 (August 11, 2021): 21–27. http://dx.doi.org/10.18412/1816-0395-2021-8-21-27.

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The possibilities of using satellite imagery of modern remote sensing satellites, both optical and radar, to study anthropogenic pollution and the state of the marine environment of the Kerch Strait are discussed. It is shown that satellite data and images allow one to quickly obtain practically complete information about a particular phenomenon and emergency situation in the strait.

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48

Bondur, Valery, and Viktor Zamshin. "Study of Intensive Anthropogenic Impacts of Submerged Wastewater Discharges on Marine Water Areas Using Satellite Imagery." Journal of Marine Science and Engineering 10, no. 11 (November 15, 2022): 1759. http://dx.doi.org/10.3390/jmse10111759.

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This paper focuses on a detailed analysis of coastal waters under the conditions of the intense anthropogenic impacts of submerged wastewater discharges, using optical and radar satellite images. The features of the intense anthropogenic impacts on the coastal waters of the northern part of the Black Sea were studied, based on the processing and analysis of systematized archival satellite and sea truth data (2015–2021). Techniques based on the formation and analysis of the spatial (2-dimensional) spectra of optical and radar satellite images, normalized radar cross-section (NRCS), and the normalized spectral index are proposed. It is convincingly shown that these techniques make it possible to register and interpret the changes in the spatial structure of wind waves, as well as the changes in the optical spectral characteristics caused by submerged wastewater discharge due to the complex hydrodynamic and hydro-optical impact. A comprehensive analysis of the results of the processing of the heterogeneous satellite and sea truth data was carried out using a geographic information system. It was found that surface disturbances caused by anthropogenic impacts due to submerged wastewater discharges were detected by local “quasi-monochromatic” spectral maxima caused by the generation of short-period internal waves (wavelengths from ~30 m to ~165 m). These maxima can be registered by high-resolution optical and radar imagery. NRCS anomalies (2–4 dB contrasts), due to the surfactant films, floating jets, and turbulence related to wastewater discharge, are registered and described, as are the changes in the spectral radiance distributions in the blue and green bands of the electromagnetic spectrum.

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49

Ma, Mengyuan, Jie Chen, Wei Liu, and Wei Yang. "Ship Classification and Detection Based on CNN Using GF-3 SAR Images." Remote Sensing 10, no. 12 (December 14, 2018): 2043. http://dx.doi.org/10.3390/rs10122043.

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Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.

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50

Dong, Xiaorui, Jiansheng Li, Bing Li, Yueqin Jin, and Shufeng Miao. "Marine Oil Spill Detection from Low-Quality SAR Remote Sensing Images." Journal of Marine Science and Engineering 11, no. 8 (August 4, 2023): 1552. http://dx.doi.org/10.3390/jmse11081552.

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Oil spills pose a significant threat to the marine ecological environment. The intelligent interpretation of synthetic aperture radar (SAR) remote sensing images serves as a crucial approach to marine oil spill detection, offering the potential for real-time, continuous, and accurate monitoring. This study makes valuable contributions to the field of marine oil spill detection based on low-quality SAR images, focusing on the following key aspects: (1) We thoroughly analyze the Deep SAR Oil Spill dataset, known as the SOS dataset, a prominent resource in the domain of marine oil spill detection from low-quality SAR images, and rectify identified issues to ensure its reliability. (2) By identifying and rectifying errors in the original literature that presented the SOS dataset, and reproducing the experiments to provide accurate results, benchmark performance metrics for marine oil spill detection with low-quality SAR remote sensing images are established. (3) We propose three progressive deep learning-based marine oil spill detection methods (a direct detection method based on Transformer and UNet, a detection method based on FFDNet and TransUNet with denoising before detection, and a detection method based on integrated multi-model learning) and the performance advantages of the proposed methods are verified by comparing them with semantic segmentation models such as UNet, SegNet, and DeepLabV3+. (4) We introduce a feasible, highly robust and easily scalable system architecture approach that effectively addresses practical engineering applications. This paper is an important addition to the research on marine oil spill detection from low-quality SAR images, and the proposed experimental method and performance details can provide a reference for related research.

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