“Significant progress has been made in the research of superpixel generation technology for multi temporal and multi polarized SAR data. In response to the problem that single phase superpixel segmentation methods cannot fully utilize the complete temporal scattering information of ground objects, researchers propose an adaptive superpixel generation method for multi temporal polarimetric SAR images based on a simple linear iterative clustering (SLIC) model. This method combines the polarization covariance matrices of multiple time phases, calculates the temporal polarization SAR similarity distance based on Wishart distribution, and uses multi temporal polarization SAR data for gradient calculation and edge detection. By introducing homogeneity measurement factors based on multi temporal polarization SAR edge detection, this method can adaptively balance the weight relationship between polarization distance and spatial distance. The experimental results show that this method outperforms both single phase polarization SAR superpixel generation methods and existing multi phase polarization SAR superpixel methods in terms of visualization effect and quantitative accuracy. The superpixels can closely adhere to the boundaries of the study area. This research achievement provides a new solution for efficient processing and application of object level data processing systems, which is of great significance for the processing and application of large amounts of multi temporal and multi polarization SAR data.”