In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has
been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small
number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical
statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images
acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added
with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and
Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the
homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also
applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on
four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region,
NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend
segmentation method can detect the change of watershed effectively.
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