We propose a novel statistical distribution texton(s-texton) feature for synthetic aperture radar(SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram statistics. Experimental results on Terra SAR data demonstrate the effectiveness of the proposed s-texton feature.
C- and X-bands Synthetic Aperture Radar(SAR) images acquired from February 2009 to September 2010 were processed with Persistent Scatterer Interferometry(PS-InSAR) algorithm to investigate spatial and temporal variations in deformation over Taiyuan City, China. The spatial pattern of subsidence and the magnitude of subsidence rate are similar in the velocity field maps achieved by the algorithm from these two data sets. It shows that there are four primary subsidence centers in Taiyuan City:Xiayuan, Wujiabao, Xiaodian, Sunjiazhai, which are near the groundwater extraction wells. The maximum subsidence rate is up to 70 mm/year at Sunjiazhai. The locus of maximum subsidence has shifted from its historical location in the north to the south. In view of the severe shortage of water resources and presented features of subsidence over Taiyuan City, we inferred that excessive pumping of groundwater was the dominant reason of land subsidence.
