The collocated normalized radar backscattering cross-section measurements from the Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) and the winds from the moored buoys are used to study the effect of different sea-surface slope probability density functions (PDFs), including the Gaussian PDF, the Gram–Charlier PDF, and the Liu PDF, on the geometrical optics (GO) model predictions of the radar backscatter at low incidence angles (0 deg to 18 deg) at different sea states. First, the peakedness coefficient in the Liu distribution is determined using the collocations at the normal incidence angle, and the results indicate that the peakedness coefficient is a nonlinear function of the wind speed. Then, the performance of the modified Liu distribution, i.e., Liu distribution using the obtained peakedness coefficient estimate; the Gaussian distribution; and the Gram–Charlier distribution is analyzed. The results show that the GO model predictions with the modified Liu distribution agree best with the KuPR measurements, followed by the predictions with the Gaussian distribution, while the predictions with the Gram–Charlier distribution have larger differences as the total or the slick filtered, not the radar filtered, probability density is included in the distribution. The best-performing distribution changes with incidence angle and changes with wind speed.
Ship surveillance by remote sensing technology has become a valuable tool for protecting marine environments. In recent years, the successful launch of advanced synthetic aperture radar (SAR) sensors that have high resolution and multipolarimetric modes has enabled researchers to use SAR imagery for not only ship detection but also ship category recognition. A hierarchical ship detection and recognition scheme is proposed. The complementary information obtained from multipolarimetric modes is used to improve both the detection precision and the recognition accuracy. In the ship detection stage, a three-class fuzzy c-means clustering algorithm is used to calculate the segmenting threshold for prescreening ship candidates. To reduce the false alarm rate (FAR), we use a two-step discrimination strategy. In the first step, we fuse the detection results from multipolarimetric channels to reduce the speckle noise, ambiguities, sidelobes, and other sources of interference. In the second step, we use a binary classifier, which is trained with prior data collected on ships and nonships, to reduce the FAR even further. In the ship category recognition stage, we concatenate texture-based descriptors extracted from multiple polarmetric channels to construct a robust ship representation for category recognition. Furthermore, we construct and release a ship category database with real SAR data. We hope that it can be used to promote investigations of SAR ship recognition in the remote sensing and related academic communities. The proposed method is validated by a comprehensive experimental comparison to the state-of-the-art methods. The validation procedure showed that the proposed method outperforms all of the competing methods by about 5% and 15% in terms of ship detection and recognition, respectively.
The Bohai Sea is located in the middle latitude region, which is an important economic development zone in China. However, sea ice drift causes significant economic losses in the winter. Sea ice drifting is difficult to track due to the long satellite repeat cycles in the polar region and the rapid changes in the Bohai Sea ice. The unique characteristics of the Geostationary Ocean Color Imager (GOCI) allow tracking of sea ice drift on a daily basis with the use of 1-h time interval images (eight images per day). This study employed the GOCI data for daily 1-h sea ice drift tracking in the Bohai Sea using a maximum cross-correlation method. Sea ice drift monitoring is accomplished by tracking the distinct characteristics of sea ice samples. The sea ice drift tracking derived from the GOCI images are validated by the in-situ data and historical data in Liaodong Bay. In addition, sea ice drift in the Bohai Sea is controlled by the surface current and wind, and the current-ice drag coefficient and wind-ice drag coefficient are 0.91 and 0.03, respectively, roughly corresponding to 2.55% of the surface wind speed.
KEYWORDS: Synthetic aperture radar, Radar, Navigation systems, Receivers, Image retrieval, Global Positioning System, Data acquisition, Data analysis, Data modeling, Sensors
A ship field truthing experiment is designed and conducted in coast off Qingdao sea area. In the experiment a working
boat equipped with Differential Global Position System receivers and anemometer navigating in experiment area to
acquire vessel's location, length, width, type and sea surface wind speed. The ship detector based on constant false alarm
rate (CFAR) is applied in the simultaneously acquired ASAR image. Ship length is retrieved from ASAR image and
compared with field data. Ship's SAR imaging characteristic is analyzed based on field data and ASAR image.
Remote sensing is an indispensable means for coastal band monitoring. Using satellite remote sensing data to monitor coastline variation and to analysis the eroding, depositing features and evolution process will be of great significance for the river mouth regulation, river course planning, coastal protective project program and trend prediction of coastal evolution. So, it is necessary to establish a coastline dynamic monitoring system. The system is mainly based on remote sensing and spatial information analysis techniques. In this paper, the system framework, design methodology and system functions are described in detail. The key techniques and methods involved in the system construction are particularly discussed, and they include the data preprocessing techniques, such as cloud identification and geometry fine correction, multi-scale coast edge extracting algorithm based on MRF model and coastline tide correction model with measured data or numerical simulation result as input, and coastline dynamic analysis method based on time series analysis and spatial topological analysis. Finally, an example to apply the system to the Yellow River mouth delta is given and the process flow diagram and procedures are described. The comparison of monitoring results with manually interpreted results has verified the favorable effect of the system.
Synthetic Aperture Radar is a most useful instrument for internal wave observation. The northern of South China Sea (SCS) is one of interested sea areas to researchers, and the large amplitude and long-crested internal solitary waves in this area are often encountered in SAR images. The waves are generated by the interaction between the tidal current and shallow bottom topography in the Luzon Strait. The spatio-temporal distribution of internal solitary waves in the northern of SCS is studied by analyzing the ERS SAR images from 1995 to 2001. In the South China Sea internal wave occurs frequently from May to August, and infrequently from November to next March. The frequent occurring area of internal wave is located in north of coral reef Dongsha. Using an ERS-2 SAR image the amplitudes of internal waves in Xijiang oil field in Northern of South China Sea are estimated by one-dimensional model with good result compared with data in situ.
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