Статті в журналах: "Images radar marin"

1

Cai, Jun, George A. McMechan, and Michael A. Fisher. "Application of ground-penetrating radar to investigation of near-surface fault properties in the San Francisco Bay region." Bulletin of the Seismological Society of America 86, no. 5 (October 1, 1996): 1459–70. http://dx.doi.org/10.1785/bssa0860051459.

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Abstract In many geologic environments, ground-penetrating radar (GPR) provides high-resolution images of near-surface Earth structure. GPR data collection is nondestructive and very economical. The scale of features detected by GPR lies between those imaged by high-resolution seismic reflection surveys and those exposed in trenches and is therefore potentially complementary to traditional techniques for fault location and mapping. Sixty-two GPR profiles were collected at 12 sites in the San Francisco Bay region. Results show that GPR data correlate with large-scale features in existing trench observations, can be used to locate faults where they are buried or where their positions are not well known, and can identify previously unknown fault segments. The best data acquired were on a profile across the San Andreas fault, traversing Pleistocene terrace deposits south of Olema in Marin County; this profile shows a complicated multi-branched fault system from the ground surface down to about 40 m, the maximum depth for which data were recorded.

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Chen, Duo, Ying Li, Yi Wen Wang, and Jin Xu. "Research on Marine Radar Image Collection Technology Based on OpenCV." Advanced Materials Research 798-799 (September 2013): 578–81. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.578.

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Marine radar image collection technology has been applied in many fileds. It has been a research focus at home and abroad for a long time. This paper proposes an architecture of marine radar image collection system based on Sperry radar, HPX Rader Information Board, OpenCV, SPX Function Library. And implementation of key technologies was diccussed from three aspects, includ-ing radar image display, collection and clear functions. This system has worked well in practice.

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3

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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4

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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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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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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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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8

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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9

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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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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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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12

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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13

Jaenicke, Julia, Christoph Mayer, Kilian Scharrer, Ulrich Münzer, and Agúst Gudmundsson. "The use of remote-sensing data for mass-balance studies at Mýrdalsjökull ice cap, Iceland." Journal of Glaciology 52, no. 179 (2006): 565–73. http://dx.doi.org/10.3189/172756506781828340.

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AbstractA series of satellite images of Mýrdalsjökull, Iceland, was analyzed in view of their value for mass-balance investigations. A combination of optical satellite images from the ASTER sensor and synthetic aperture radar data from ERS-2 and Envisat ASAR proved very useful. The glacier margin of Mýrdalsjökull was delineated on ASTER images from summer and winter 2004. With a time series of summer ASAR images it was possible to monitor the temporal and spatial development of the transient snowline (TSL) throughout the year 2004, as well as the firn line (FL) at the end of the balance year. An ‘inverse’ function was applied to visually enhance detail in the radar imagery. Winter radar images were not useful for mass-balance observations because of frequent surface melting, which prevented the transparency of the snow cover for C-band microwaves. Interannual mass-balance fluctuations were observed by comparing three radar images acquired in late summer 1998, 1999 and 2004 respectively. These fluctuations follow the same trend as the annual mean air temperature which shows a strong increasing trend between 1999 and 2004. An accumulation-area ratio of <0.43 was determined for 2004, indicating clear negative mass-balance conditions. Monitoring the TSL-FL with radar summer images for mass-balance studies, rather than the equilibrium line (EL), is suggested for large ice caps in maritime climates.

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14

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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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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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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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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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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Egset, Cathrine N., and Elisabeth Nost. "Automatic Oil Spill Detection by Marine X-Band Radars -New System Based on Processing of Digitized Radar Images." Journal of The Japan Institute of Marine Engineering 42, no. 5 (2007): 769–74. http://dx.doi.org/10.5988/jime.42.5_769.

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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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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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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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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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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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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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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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27

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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28

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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29

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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30

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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31

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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32

Robinson, I. S., N. P. Ward, C. P. Gommenginger, and M. A. Tenorio-Gonzales. "Coastal Oceanography Applications of Digital Image Data from Marine Radar." Journal of Atmospheric and Oceanic Technology 17, no. 5 (May 2000): 721–35. http://dx.doi.org/10.1175/1520-0426(2000)017<0721:coaodi>2.0.co;2.

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33

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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34

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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35

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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36

Hall, Dorothy K., Richard S. Williams, and Oddur Sigurdsson. "Glaciological observations of Brúarjökull, Iceland, using synthetic aperture radar and thematic mapper satellite data." Annals of Glaciology 21 (1995): 271–76. http://dx.doi.org/10.3189/s0260305500015937.

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The first European Remote Sensing Satellite (ERS-1) synthetic aperture radar (SAR) images offer opportunities for studying glacier surface properties and near-surface features. Analysis of back-scatter values from digital SAR data from 18 January, 7 June, 1 September and 25 October 1993 of Brúarjökull, an outlet glacier on the northeastern margin of the Vatnajökull ice cap, Iceland, that has a history of episodic surges, reveals several back-scatter boundaries that may relate to glacier facies and, inferentially, to mass balance. For example, a strong back-scatter boundary on the 18 January image of the snow-covered glacier, representing a back-scatter coefficient, σ°, difference of 4.34dB, appears to coincide with the position of the transient snow line at the end of the 1990–91 budget year. The boundary is visible on the 7 September 1991 Landsat thematic mapper (TM) image. The terminus is very difficult to define because of back-wasting from the last surge (1963–64) but is most easily delineated on the 1 September 1993 SAR and the 7 September 1991 TM images, in part due to the presence of ice-margin lakes.

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37

Hall, Dorothy K., Richard S. Williams, and Oddur Sigurdsson. "Glaciological observations of Brúarjökull, Iceland, using synthetic aperture radar and thematic mapper satellite data." Annals of Glaciology 21 (1995): 271–76. http://dx.doi.org/10.1017/s0260305500015937.

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Анотація:

The first European Remote Sensing Satellite (ERS-1) synthetic aperture radar (SAR) images offer opportunities for studying glacier surface properties and near-surface features. Analysis of back-scatter values from digital SAR data from 18 January, 7 June, 1 September and 25 October 1993 of Brúarjökull, an outlet glacier on the northeastern margin of the Vatnajökull ice cap, Iceland, that has a history of episodic surges, reveals several back-scatter boundaries that may relate to glacier facies and, inferentially, to mass balance. For example, a strong back-scatter boundary on the 18 January image of the snow-covered glacier, representing a back-scatter coefficient, σ°, difference of 4.34dB, appears to coincide with the position of the transient snow line at the end of the 1990–91 budget year. The boundary is visible on the 7 September 1991 Landsat thematic mapper (TM) image. The terminus is very difficult to define because of back-wasting from the last surge (1963–64) but is most easily delineated on the 1 September 1993 SAR and the 7 September 1991 TM images, in part due to the presence of ice-margin lakes.

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38

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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39

Liu, Liqiang, Yuntao Dai, and Jinyu Gao. "Simulation Analysis and Model of Current Retrieval Based on Marine Radar Sea Clutter Images." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/173948.

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Using the sea clutter image from X-Band radar for current retrieval is an effective way of obtaining information on ocean currents. Traditional methods used for current retrieval have been based on the least squares algorithm, which is not only simple and efficient but also generally speaking accurate. In order to improve the precision of current retrieval, this paper has, as its goal, the study of the used radar connected with sea clutter imaging for current retrieval, with the particle swarm optimization (PSO) algorithm being proposed. This method is achieved by obtaining a three-dimensional image spectrum, taking the high-order dispersion relation model as the theoretical distribution model of the wave energy points of three-dimensional image spectra, using a forward model within the PSO framework, and considering the requirements of the order of the model, weights and optimal distribution of the energy points, and so on in fitness function. Simulation results show that, compared with the traditional ILSM methods, the method provided in this paper is more flexible, with a capacity for a high dispersion relationship order, higher precision, and an increased stability in terms of current inversion.

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40

Wu, Li-Chung, Dong-Jiing Doong, and Jong-Hao Wang. "Bathymetry Determination From Marine Radar Image Sequences Using the Hilbert Transform." IEEE Geoscience and Remote Sensing Letters 14, no. 5 (May 2017): 644–48. http://dx.doi.org/10.1109/lgrs.2017.2668383.

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41

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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42

Lucchitta, B. K., and C. E. Rosanova. "Retreat of northern margins of George VI and Wilkins Ice Shelves, Antarctic Peninsula." Annals of Glaciology 27 (1998): 41–46. http://dx.doi.org/10.3189/1998aog27-1-41-46.

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The George VI and Wilkins Ice Shelves are considered at risk of disintegration due to a regional atmospheric warming trend on the Antarctic Peninsula. Retreat of the northern margin of the George VI Ice Shelf has been observed previously, but the Wilkins Ice Shelf was thought to be stable. We investigated the positions of the northern fronts of these shelves from the literature and looked for changes on 1974 Landsat and 1992 and 1995 European remote-sensing satellite (ERS) synthetic aperture radar images. Our investigation shows that the northern George VI Ice Shelf lost a total of 906 km2 between 1974 and 1992, and an additional 87 km2 by 1995. The northern margin of the Wilkins Ice Shelf lost 796 km2 between 1990 and 1992, and another 564 km2 between 1992 and 1995. Armadas of tabular icebergs were visible in front of this shelf in the ERS images. These two ice shelves mark the southernmost documented conspicuous retreat of ice-shell margins.

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43

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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44

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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45

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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46

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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47

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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48

Fahnestock, Mark A., and Robert A. Bindschadler. "Description of a program for SAR investigation of the Greenland ice sheet and an example of margin change detection using SAR." Annals of Glaciology 17 (1993): 332–36. http://dx.doi.org/10.1017/s0260305500013069.

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In order to interpret changes in the Greenland ice sheet as indications of climatic variation, it is necessary to place observations of local changes in a regional context. This requires a comprehensive monitoring effort which addresses both the inland ice and changes in the ice margin. This paper describes the design of a program for regional investigation of the Greenland ice sheet using synthetic aperture radar (SAR), and discusses the utility of SAR data for detection of change in the ice sheet margin. Comprehensive coverage of the Greenland ice sheet by ERS-1 SAR will allow us to map the boundaries of snow facies on the ice sheet and investigate recent changes in the ice margin. We will use geo-referenced images to map the current boundaries of snow facies, providing a baseline which can be used to detect future change. We demonstrate the utility of SAR for detecting recent changes in the ice margin. SAR images clearly show the ice edge, moraines, and ice marginal lakes. These features can be compared with published maps and earlier images in order to document changes in the margin of the ice sheet. We show evidence for recession of a section of the western margin of the ice sheet. The recession, which occurred between 1938 and 1978, ranges from a few hundred meters at high elevations to several kilometers at calving faces in both ice marginal lakes and fjords. ERS-1 SAR will provide the first opportunity to pursue a comprehensive investigation of the state of the Greenland ice sheet. The ability to address conditions on the ice and to look at margin changes in a systematic way will allow us to develop a stronger framework for interpreting changes in the ice sheet in terms of climatic variation.

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49

Li, Xiao Yong, Jing Wang, Mei Ling Sun, Rui Ling Ma, and Jun Min Meng. "Internal Wave Parameter Inversion at Malin Shelf Edge Based on the Nonlinear Schrödinger Equation." Applied Mechanics and Materials 441 (December 2013): 388–92. http://dx.doi.org/10.4028/www.scientific.net/amm.441.388.

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We established a deep-sea internal wave detection model based on the nonlinear Schrödinger (NLS) equation and Synthetic Aperture Radar (SAR) images, and applied the model to the Malin Shelf edge, located at UK Continental Shelf, west of Scotland, to retrieve internal wave parameters. We selected the SAR images of internal waves at Malin Shelf edge, combined NLS equation with the action spectrum balance equation and Bragg scattering model, retrieved the amplitudes and phase velocities of the internal waves at Malin Shelf edge, and compared these data with those retrieved by the model based on KdV equation and those observed at the same period. The results show that the error between the data retrieved by our model and the measured data is very small, while the difference between the data retrieved by the detection model based on KdV equation and the measured data is significant. In addition, the phase velocities, calculated in our model and the model based on KdV equation, are both close to the measured data. Consequently, our model is valid and more accurate for the parameter inversion of internal waves in deep-sea area.

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50

Campbell, Seth, Greg Balco, Claire Todd, Howard Conway, Kathleen Huybers, Christopher Simmons, and Michael Vermeulen. "Radar-detected englacial stratigraphy in the Pensacola Mountains, Antarctica: implications for recent changes in ice flow and accumulation." Annals of Glaciology 54, no. 63 (2013): 91–100. http://dx.doi.org/10.3189/2013aog63a371.

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AbstractWe used measurements of radar-detected stratigraphy, surface ice-flow velocities and accumulation rates to investigate relationships between local valley-glacier and regional ice-sheet dynamics in and around the Schmidt Hills, Pensacola Mountains, Antarctica. Ground-penetrating radar profiles were collected perpendicular to the long axis of the Schmidt Hills and the margin of Foundation Ice Stream (FIS). Within the valley confines, the glacier consists of blue ice, and profiles show internal stratigraphy dipping steeply toward the nunataks and truncated at the present-day ablation surface. Below the valley confines, the blue ice is overlain by firn. Data show that upward-progressing overlap of actively accumulating firn onto valley-glacier ice is slightly less than ice flow out of the valleys over the past ∼1200 years. The apparent slightly negative mass balance (-0.25 cm a-1) suggests that ice-margin elevations in the Schmidt Hills may have lowered over this time period, even without a change in the surface elevation of FIS. Results suggest that (1) mass-balance gradients between local valley glaciers and regional ice sheets should be considered when using local information to estimate regional ice surface elevation changes; and (2) interpretation of shallow ice structures imaged with radar can provide information about local ice elevation changes and stability.

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