Most of the traditional supervised classification methods using full-polarimetric synthetic aperture radar (PolSAR) imagery are dependent on sufficient training samples, whereas the results of pixel-based supervised classification methods show a high false alarm rate due to the influence of speckle noise. In this paper, to solve these problems, an object-based supervised classification method with an active learning (AL) method and random forest (RF) classifier is presented, which can enhance the classification performance for PolSAR imagery. The first step of the proposed method is used to reduce the influence of speckle noise through the generalized statistical region merging (GSRM) algorithm. A reliable training set is then selected from the different polarimetric features of the PolSAR imagery by the AL method. Finally, the RF classifier is applied to identify the different types of land cover in the three PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method can not only better suppress the influence of speckle noise, but can also significantly improve the overall accuracy and Kappa coefficient of the classification results, when compared with the traditional supervised classification methods.

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Soil moisture is a key parameter affecting crop growth. Gaofen-3 (GF-3) satellite is the first C-band synthetic aperture radar (SAR) produced by China, which provides fullpolarization data sources for soil moisture estimation. This paper evaluated the potential of estimating soil moisture via GF-3 SAR over agricultural area using different polarimetric decomposition models, namely, the Modified Freeman-Durden Model (MFDM), the An Model (AM) and the Freeman-Durden Model (FDM). Among them, the MFDM is the first attempt to be used for soil moisture retrieval. After removing the volume scattering, the surface and dihedral scattering component were used complementarily to estimate soil moisture. The results show the performance of each polarimetric decomposition models for soil moisture estimation depends on the crop type, crop growth stages and soil moisture conditions. Soil moisture retrievals exhibit an overall underestimation with a root mean square error of 8-11vol. %. This is mainly because of the random orientation assumption in the volume scattering model, which cannot accurately describe the variability of the crop structure. Due to the application of de-orientation process and power constraint, the MFDM shows the best performance both for corn and wheat, with inversion rates of 39%-45%.