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 ( $S\alpha S$ ) 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 ( $S\alpha S$ ) 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.

Saltpans extraction is vital for coastal resource utilization and production management. However, it is challenging to extract saltpans, even by visual inspection, because of their spatial and spectral similarities with aquaculture ponds. Saltpans are composed of crystallization and evaporation ponds. From the whole images, existing saltpans extraction algorithms could only extract part of the saltpans, i.e., crystallization ponds. Meanwhile, evaporation ponds could not be efficiently extracted by only spectral analysis, causing the degeneration of saltpans extraction. In addition, manual intervention was required. Thus, it is essential to study the automatic saltpans extraction algorithm of the whole image. As to the abovementioned problems, this paper proposed a novel method with an amendatory saltpan index (ASI) and local spatial parallel similarity (ASI-LSPS) for extracting coastal saltpans. To highlight saltpans and aquaculture ponds in coastal water, the Hessian matrix has been exploited. Then, a new amendatory saltpans index (ASI) is proposed to extract crystallization ponds to reduce the negative influence of turbid water and dams. Finally, a new local parallel similarity criterion is proposed to extract evaporation ponds. The Landsat-8 OLI images of Tianjin and Dongying, China, have been used in experiments. Experiments have shown that ASI can reach at least 70% in intersection over union (IOU) and 78% in Kappa for extraction of crystallization in saltpans. Moreover, experiments also demonstrate that ASI-LSPS can reach at least 82% in IOU and 89% in Kappa on saltpans extraction, at least 13% and 17% better than comparing algorithms in IOU and Kappa, respectively. Furthermore, the ASI-LSPS algorithm has the advantage of automaticity in the whole imagery. Thus, this study can provide help in coastal saltpans management and scientific utilization of coastal resources.

To overcome the limitation of performance degradation of existing methods based on fractional Fourier transform in impulsive noise, and fractional lower-order statistics based method dependence on a priori knowledge of the noise, a novel Sigmoid fractional Fourier transform (Sigmoid-FRFT) is presented in this paper. This novel approach is then used to estimate the Doppler stretch and time delay. Furthermore, the properties of the Sigmoid transform, robustness and boundedness of the Sigmoid-FRFT to the $S\alpha S$ noise, and the computation complexity of the Sigmoid-FRFT method are presented to evaluate the performance of the proposed method. Simulation results and theoretical analysis are presented to demonstrate the applicability of the forgoing method. It is shown that the proposed method not only can effectively suppress impulsive noise interference but also does not need a priori knowledge of the noise, with higher estimation accuracy and lower computational complexity in impulsive noise environments.

Reliability factors in Markov random field (MRF) could be used to improve classification performance for synthetic aperture radar (SAR) and optical images; however, insufficient utilization of reliability factors based on characteristics of different sources leaves more room for classification improvement. To solve this problem, a Markov random field (MRF) with amendment reliability factors classification algorithm (MRF-ARF) is proposed. The ARF is constructed based on the coarse label field of urban region, and different controlling factors are utilized for different sensor data. Then, ARF is involved into the data energy of MRF, to classify the sand, vegetation, farmland, and urban regions, with the gray level co-occurrence matrix textures of Sentinel-1 imagery and the spectral values of the Landsat 8 imagery. In the experiments, Sentinel-1 and Landsat-8 images are used with overall accuracy and Kappa coefficient to evaluate the proposed algorithm with other algorithms. Results show that the overall accuracy of the proposed algorithm has the superiority of about 20% in overall precision and at least 0.2 in Kappa coefficient than the comparison algorithms. Thus, the problem of insufficient utilization of different sensors data could be solved.