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Abstract:In this paper, we consider retrieving individual wave components in a multi-directional sea wave model.To solve this problem, a currently and commonly used method is three-dimensional discrete Fourier transform (3D DFT) on the radar image sequence. However, the uniform frequency and the uniform wavenumber in a wavenumber frequency domain can not always strictly satisfy the dispersion relation, and the spectral leakage in both temporal and spatial domains exists due to the limited analysis area selected from an image sequence. As a result, the DFT method incurs undesirable error performance in retrieving directional wave components. By deeply investigating the data structure of the multi-directional sea wave model, we obtain a new and decomposable matrix representation for processing the wave components. Then, a novel successive cancellation method is proposed to efficiently and effectively extract individual wave components, whose frequency and wavenumber rigorously satisfy the liner dispersion relation. Thus, it avoids spectral leakage in the spatial domain. The algorithm is evaluated by using linear synthetic wave image sequences. The validity of the proposed novel algorithm is verified by comparing the retrieved parameters of amplitude, phase, and direction of the individual wave components with the simulated parameters as well as those obtained by using the 3D DFT method. In addition, the reconstructed sea field using the retrieved wave components is also compared with the simulated remote sensing images as well as those attained using the inverse 3D DFT method.All the simulation results demonstrate that our proposed algorithm is more effective and has better performance for retrieving individual wave components from the spatio-temporal remote sensing image sequences than the 3D DFT method.

In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.

In this study, an energy spectrum (ES) algorithm is proposed to retrieve wind direction from Xband marine radar image sequences. This algorithm is based on utilizing the occlusion area zero-pixel percentage (OZPP) to distinguish rain-free and rain-contaminated radar data. And then the rain-contaminated images are detected and discarded. The effect of radar radial attenuation in radar image sequences is modified by the piecewise fitting technique. Wind direction is determined from rain-free and radial correction data, based on the energy spectrum of small-scale wind streaks. The energy spectrum of small-scale wind streaks is obtained by establishing an energy spectrum scale separation filter. Based on the wind streak characteristics, a two-dimensional fast Fourier transform (FFT) is used to obtain the energy spectrum of radar images. The wind streak characteristics are derived from the distribution of the azimuth normalization radar cross-section (NRCS). The proposed algorithm is tested using data collected from X-band radar images and in-situ anemometer data from the coast of the East China Sea. Compared with the anemometer data, after using the proposed algorithm, the root-mean-square difference for wind direction is 12.13°, which is an acceptable result for engineering application.

Abstract:In this paper, the retrieving significant wave height from X-band marine radar images based on shadow statistics is investigated, since the retrieving accuracy can not be seriously affected by environmental factors and the method has the advantage of without any external reference to calibrate. However, the accuracy of the significant wave height estimated from the radar image acquired at the near-shore area is not ideal. To solve this problem, the effect of water depth is considered in the theoretical derivation of estimated wave height based on the sea surface slope. And then, an improved retrieving algorithm which is suitable for both in deep water area and shallow water area is developed. In addition, the radar data are sparsely processed in advance in order to achieve high quality edge image for the requirement of shadow statistic algorithm, since the high resolution radar images will lead to angle-blurred for the image edge detection and time-consuming in the estimation of sea surface slope. The data acquired from Pingtan Test Base in Fujian Province were used to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the improved method which takes into account the water depth is more efficient and effective and has better performance for retrieving significant wave height in the shallow water area, compared to the in situ buoy data as the ground truth and that of the existing shadow statistic method.

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