Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

Cropland classification can be used to monitor cropland distribution and its change over time. In this letter, a new superpixel-based cropland classification method is proposed for synthetic aperture radar (SAR) imagery through the integration of statistical texture, polarization, and spatial information. First, the method combines random forest algorithm and superpixels, which are generated using simple linear iterative clustering algorithm with polarization features of Pauli decomposition and spatial information. Superpixel-based spatial context information is used to reduce the influence of coherent speckle and misclassification in cropland blocks. Second, $G^{0}$ statistical texture feature is used to reduce the interference of background targets such as woodland in cropland classification. Comparison experiments of different methods using C-band airborne SAR (AIRSAR) polarimetric data acquired in early July show that the proposed method has better classification performance, with an overall accuracy of 88.62%. The classification accuracy of corn and soybean is above 95% and 91%, respectively. The $G^{0}$ statistical texture feature is helpful to eliminate woodland that may cause crop misclassification using single-date SAR image.

Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.

Texture features are important characteristics in distinguishing collapsed buildings and intact buildings. However, texture features currently used in synthetic aperture radar (SAR) building damage assessment are extracted following the methods of optical images directly, which do not consider the statistical feature of speckles and limit the accuracy improving. Therefore, a statistical texture feature—G0-para—was proposed to reflect the homogeneity of buildings in complex urban areas after a disaster. The G0-para is arising from the G0 distribution of SAR image and used to distinguish collapse buildings and intact buildings. First, the G0-para is unified to satisfy different polarization data—single-/dual-/quad-/compact-polarization. Second, the distinguishing ability of G0-para is under comparison in single-/dual-/quad-/compact-polarization, through the receiver operating characteristic (ROC) curve and the area under the ROC curve analysis. Then, collapsed buildings with RADARSAT-2 and ALOS-1 data are evaluated, selecting the optimal combinations of each mode and comparing with the preferable existing texture features. The results show that the statistical texture parameter—G0-para—is better than the variance of gray-level histogram and the contrast of gray level co-occurrence matrix in distinguishing intact buildings and collapsed ones and G0-para can be applied to single-/dual-/quad-/compact-polarimetric SAR data. For experimental data, VV and HH in single polarization, VH/VV and HH/HV in dual polarization, and hybrid mode in compact polarization are recommended when the best quad polarization is unavailable.

Accurately predicting large-scale surface displacements is a vital task in the prevention and control of geological hazards in mining areas. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) faces challenges in capturing significant deformations, and implementing extensive global navigation satellite system (GNSS) monitoring can only obtain a single point of deformation. To combine the strengths of both techniques, we proposed a novel approach named the LL-SIG (Integrating single SBAS-InSAR pair, GNSS and Log-logistic model), which leverages the understanding of mining subsidence patterns and integrates single-orbit InSAR data with limited GNSS data to achieve overall and high-gradient three-dimensional (3-D) deformation monitoring and prediction in mining areas. Firstly, we employed empirical Bayesian kriging interpolation to integrate GNSS and SBAS-InSAR data at a consistent time interval. This allowed us to obtain an overall large-gradient deformation pattern of the mining area based on its subsidence characteristics. Subsequently, we combined the Log-logistic model with the SIP (single InSAR pair) model to calculate the 3-D deformation resulting from mining activities at any moment. The proposed method was tested in the Fenxi Ruitai mine. The root mean square error (RMSE) of the fitted deformation in the line-of-sight (LOS) direction was within 10% of the maximum deformation. Compared to the Logistic model and Gompertz model, the Log-logistic model show superior accuracy and efficiency in mine deformation prediction under the same testing conditions. The RMSE of deformation for this method was 3.20 cm, 5.85 cm, and 3.73 cm in the vertical, northward, and eastward directions, respectively.

Retrieving thunderstorm activity through specific thermodynamic and kinematic parameters is paramount for predicting deep convective weather and investigating long‐term climatology of storm. However, the reliability of the relationship between parameters and convective events is restricted by the modelling methods and sampling of thunderstorm activity. There is no objective definition of a thunderstorm, so the clustering method is applied to the cloud‐to‐ground (CG) lightning stroke data in Central China to identify lightning clusters. These clusters are then gridded and associated with environmental variables derived from ERA5 reanalysis. Finally, machine learning (ML) technologies are applied to model the occurrence of thunderstorms. In addition, ERA5 is also evaluated. The parameters related to moisture and lapse rate calculated by ERA5 are close to sounding measurements, such as Td850, PW, LR700_400 and KI, whose correlation coefficients exceed 0.90. ERA5 has a good estimation of some parameters that are susceptible to the influence of the boundary layer. Compared with the lightning strike‐based scheme, our scheme obtains the best performance index values. An agreement between observations and predictions based on lightning clusters is also evident from the diurnal cycle of thunderstorm probabilities. Although thunderstorm activity on complex terrain is underestimated, the created ML model can explain 61.4% of the variance in the observed frequency. The results of the significance test reveal that there are statistically significant differences between the soundings corresponding to some isolated CG strikes and the thunderstorm class, but the distribution is the same as that of the non‐thunderstorm class. Solar radiation, topographic features and lake distribution play a major role in promoting the occurrence and development of thunderstorms.