An Energy Spectrum Algorithm for Wind Direction Retrieval From X-Band Marine Radar Image Sequences

Abstract

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.

Wind-Direction Estimation from Single X-Band Marine Radar Image Improvement by Utilizing the DWT and Azimuth-Scale Expansion Method

“…The zero-pixel percentage formula, from [ 18 ], is where is the total number of pixels in the fixed occlusion area, and is the total number of zero-pixels in the fixed occlusion area. The data used in this study and those in [ 26 ] are from the same radar system; referring to its rainfall recognition threshold, 0.94 was selected as the threshold to detect whether the radar image was affected by rainfall. When the OZPP was greater than 0.94, it meant that there was no rainfall; when the OZPP was less than 0.94, the image was polluted by rain, and the contaminated image was discarded.…”

“…In addition, the method for retrieving wind directions also considers the characteristics of wind-induced streaks in radar images. Most of these features are associated with wind streaks or streaks from foam or surfactants, which are in the scale from 200 to 500 m [ 26 ]. However, in marine-radar images, these wind streaks are superimposed by other ocean features and are barely visible.…”

“…On this basis, Ref. [ 26 ] put forward the energy spectrum method (ESM), which obtains the wave-number energy spectrum of small-scale wind streaks with a two-dimensional Fast Fourier transform. However, this method can only be used on fixed platforms.…”

“…Then, the sub-image with the appropriate scale is selected among the low-frequency sub-images of different scales. Compared with the method in [ 26 ], the wavelet transform of a single image can reduce the risk of wind features being obliterated due to ship movement. Finally, the wind direction is extracted from the relationship between the radar return and the antenna look azimuth.…”

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

“…In 2016, Wang, Y. and Huang, W. [25] proposed that sea surface wind direction information can be obtained under rainfall and nonrainfall conditions using the Fourier transform wavenumber spectrum region and the value of the radar image, respectively. A wind field energy spectrum method (ESM) with the characteristic that the axis of the small-scale wind streak is parallel to the sea surface wind direction is proposed in [26]. However, this kind of method can only be used when the image coverage is greater than 180 • , which is highly dependent on the streak scale characteristics.…”