Xiaofei Shi

In single-polarization synthetic aperture radar imagery, coastline extraction is a challenging task due to complex coastal region condition, low incidence angles (<; 30°), VV or HH polarization, speckle and sea wind, which have to be handled by visual inspection and manual operation for coastline extraction. To solve this problem, this article proposes a coastline extraction algorithm by utilizing superpixel as the basic unit. An improved line finder is presented and involved in the simple linear iterative clustering to obtain better results around the dam. Then, Gabor feature is extracted to characterize the superpixels. Finally, the hidden Markov random field model is utilized to classify superpixels. A coarse to fine strategy is utilized to extract coastline for the experimental imagery. Four VV polarization ENVISAT and ERS images with incidence angle (30.9°-36.6°, 17°-42°, 17°-42°, 19°-26°) are used as the experimental imagery. Four sites in each imagery are chosen to evaluate the performance of proposed superpixel-based algorithm under the condition of complex coastal region such as a thin dam, sharp corner, weak boundary, together with low-to-moderate sea wind. By using the manually traced coastlines as the standards in the qualitative evaluations with the extracted results, with mean offset (MO) in pixels, root-mean-square error (RMSE) in pixels, proportion of overlapped pixels (OLP) in percentage, distance within one pixel (WOP) in percentage, and distance within two pixels (WTP) in percentage as performance comparison, the accuracy improvements in detecting coastline along the dam are at least about 0.08, 0.19, 2.07% in MO, RMSE, and OLP. The accuracy improvements of the sharp corner are at least about 0.07, 0.09, 2.44%, 2.69%, and 2.77%. The accuracy improvements of the weak boundary are about 2.63, 2.99, 5.26%, 11.46%, and 13.96%. While the proposed algorithm shows weaknesses with the comparison algorithms in WOP, WTP for the dam, OLP, WOP, and WTP for sea wind. In addition, the processing time of the proposed algorithm is a burden to obtain better accuracy performance. Experimental results show that the proposed coastline extraction algorithm is more effective and suitable for coastline extraction in comparison with other existing algorithms in the dam, weak boundary, and sharp corner.

Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple-output (MIMO) radar systems under impulsive noise. In this paper, a novel method based on Sigmoid transform was used to estimate target parameters, which do not need a priori knowledge of the noise in an impulsive noise environment. Firstly, a novel wideband ambiguity function, termed Sigmoid wideband ambiguity function (Sigmoid-WBAF), is proposed to estimate the Doppler stretch and time delay by searching the peak of the Sigmoid-WBAF. A novel Sigmoid correlation function is proposed. Furthermore, a new MUSIC algorithm based on the Sigmoid correlation function (Sigmoid-MUSIC) is proposed to estimate the direction-of-departure (DOD) and direction-of-arrival (DOA). Then, the boundness of the Sigmoid-WBAF to the symmetric alpha stable () noise, the feasibility analysis of the Sigmoid-WBAF, and complexity analysis of the Sigmoid-WBAF and Sigmoid-MUSIC are presented to evaluate the performance of the proposed method. In addition, the Cramér⁻Rao bound for parameter estimation was derived and computed in closed form, which shows that better performance was achieved. Simulation results and theoretical analyses are presented to verify the effectiveness of the proposed method.

In this paper, a novel method, that employs a fractional Fourier transform and a tuneable Sigmoid transform, is proposed, in order to estimate the Doppler stretch and time delay of wideband echoes for a linear frequency modulation (LFM) pulse radar in an alpha-stable distribution noise environment. Two novel functions, a tuneable Sigmoid fractional correlation function (TS-FC) and a tuneable Sigmoid fractional power spectrum density (TS-FPSD), are presented in this paper. The novel algorithm based on the TS-FPSD is then proposed to estimate the Doppler stretch and the time delay. Then, the derivation of unbiasedness and consistency is presented. Furthermore, the boundness of the TS-FPSD to the symmetric alpha stable ( S α S ) noise, the parameter selection of the TS-FPSD, and the feasibility analysis of the TS-FPSD, are presented to evaluate the performance of the proposed method. In addition, the Cramér⁻Rao bound for parameter estimation is derived and computed in closed form, which shows that better performance has been achieved. Simulation results and theoretical analysis are presented, to demonstrate the applicability of the forgoing method. It is shown that the proposed method can not only effectively suppress impulsive noise interference, but it also does not need a priori knowledge of the noise with higher estimation accuracy in alpha-stable distribution noise environments.