Zhongbiao Chen

A new process for the segmentation of high resolution remote sensing imagery

The “watershed transformation” is a well‐known powerful tool for automated image segmentation. However, it is often computationally expensive and can produce over‐segmentation in situations of high gradient noise, quantity error and detailed texture. Here, a new method has been designed to overcome these inherent drawbacks. After pre‐processing the imagery using a nonlinear filter in order to filter the noise, an optimized watershed transformation is applied to provide an initial segmentation result. Then, a multi‐scale, multi‐characteristic merging algorithm is used to refine the segmentation. Preliminary results show promise in term of both segmentation quality and computational efficiency.

A New Algorithm to Retrieve Wave Parameters From Marine X-Band Radar Image Sequences

A new algorithm to retrieve wave parameters, such as significant wave height (SWH), peak wave period, wavelength, and wave direction, from marine X-band radar image sequences is proposed. To analyze the heterogeneous nearshore wave field, the empirical orthogonal function is used to extract the principal components (PCs) of the radar image sequence. To retrieve the SWH, a linear relationship between SWH and the standard deviation of PC is established. The maximum entropy power spectral density (PSD) of the first PC is used to derive the peak wave period. The PSD on spatial scale is used to estimate two perpendicular components of the wavelength, and then, the wavelength and wave direction are determined by geometric relationship. Inversion schemes are validated by comparing with data from the in situ buoy; the root-mean-square error between the wave parameters retrieved from radar image sequences and measured by the buoy is 0.21 m for SWH and 0.84 s for the peak wave period, and the biases for these are 0.01 m and 0.33 s.

A new method to retrieve significant wave height from X-band marine radar image sequences

A new method is proposed to retrieve significant wave height (SWH) from X-band marine radar image sequences. To reduce the inhomogeneity of the nearshore wave field, the principal component (PC) of the radar image sequence is extracted by empirical orthogonal function (EOF) analysis. To measure the information contained in each PC, the Shannon entropy is introduced after the PC is normalized. Based on the information contained in the wave field, a linear relationship is established to retrieve the SWH from the Shannon entropy of the PC. The method is validated by comparison with measurements from in situ buoys: the root mean square error between the SWH measured by a buoy and the retrieved value is 0.22 m, while the corresponding bias and correlation coefficient are 0.01 m and 0.92, respectively. The physical meanings of different EOF modes decomposed from the wave field are also discussed.

An Automatic Algorithm to Retrieve Wave Height From X-Band Marine Radar Image Sequence

A new method is proposed to retrieve wave height from an X-band marine radar image sequence, without external measurements for reference. The X-band marine radar image sequence is first decomposed by empirical orthogonal function (EOF), and then the sea surface height profile is reconstructed and scaled from the first EOF mode. The radial profiles that are close to the peak wave direction are used to extract the zero-crossing wave periods and relative wave heights. The spectral width parameter is deduced from the histogram of a dimensionless wave period. Based on a joint probability distribution function (pdf) of a dimensionless wave period and wave height, the theoretical pdf of the wave height is derived. A shape parameter is defined for the theoretical pdf and the histogram of the relative wave heights, and then the calibration coefficient is estimated. The method is validated by comparing the significant wave heights retrieved from two different X-band marine radar systems with those measured by buoy; the correlation coefficient, the root-mean-square error, and the bias between them are 0.78, 0.51 m, and −0.19 m for HH polarization, while they are 0.77, 0.51 m, and 0.19 m for VV polarization, respectively. The sources of error of the method are discussed.

Study of Ocean Waves Measured by Collocated HH and VV Polarized X-Band Marine Radars

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.

A New Modulation Transfer Function With Range and Azimuth Dependence for Ocean Wave Spectra Retrieval From X-Band Marine Radar Observations

The conventional linear modulation transfer function (MTF) was derived using HH-polarized marine radar observations in deepwater conditions. It is possible to constrain this MTF for ocean surface wave spectra retrieval in coastal shallow waters. In this letter, we propose a new MTF with both range and azimuth dependence based on VV-polarized radar measurements acquired from heterogeneous coastal wave fields. This new MTF is determined using a radar-observed image spectrum and in situ buoy-measured wave frequency spectrum. To assess the proposed MTF, we compare the buoy-measured 1-D wavenumber spectrum with those obtained using different MTFs. Compared to the conventional linear MTF, the new MTF-derived wavenumber spectrum is closer to buoy measurements. The retrieved peak and mean wave periods are also validated using concurrent wave buoy measurements. It is shown that the retrieval accuracies of peak and mean wave periods of the new MTF are better than those of the conventional MTF. The bias and root mean square errors of the peak and mean wave periods of the new MTF are 0.52 and 0.95 s and 0.26 and 0.48 s, respectively. This suggests that the proposed new MTF is more appropriate for retrieving integral wave parameters than the conventional linear MTF.

The significant wave height distribution retrieved from marine X-band radar images

The determination of significant wave height (SWH) from marine X-band radar image sequences is based on an empirical relationship with the signal-to-noise ratio (SNR). To reduce the dependence of in-situ calibrations, the variations of SNR with the range from the radar station and the relative azimuth between the antenna looking angle and the wave propagation direction are analyzed. Then a method was developed to correlate the SNR with the fluctuations of the sea surface elevations, by taking out the variations of SNR with range and azimuth. Moreover, the method is validated using marine X-band radar image sequences from a field experiment, comparisons of the corrected SNR with a buoy show that the method gives a more accurate estimation of SWH than the uncorrected SNR does. Besides, the method can remove the influence of the noise caused by light rain as well.

Observation of tide from X-band marine radar image sequences

Tide has been monitored by tide-gauge stations and spaceborne radar altimeters. A new method to estimate the characteristics of coastal tide from X-band marine radar image sequences is proposed. The significant wave height (SWH) is firstly retrieved from X-band marine radar image sequence, and then filtered to remove the influences of noises. The main tide period is retrieved by analyzing the spectrum of the time series of filtered SWH. The change rate of the tide elevation can be obtained from the change rate of the amplitude of filtered SWH. The method is validated by comparing with the predictions from an ocean tide model.