超像素生成是对象级数据处理体系的重要预处理步骤，这对于拥有大数据量的多时相、多极化SAR数据的高效处理和应用具有很大的现实意义。针对单时相超像素分割方法没有充分利用地物在时序上完整散射信息的问题，本文综合利用时序极化SAR数据观测充分、可描述时变特性等优势，提出了一种基于简单线性迭代聚类(SLIC)模型的多时相极化SAR影像自适应协同分割方法。该方法联合多个时相的极化协方差矩阵，基于Wishart分布计算时序极化SAR相似性距离，并提出一种基于多时相极化SAR边缘检测的同质性测度因子，用于自适应平衡极化距离和空间距离的权重关系。本文使用8景Radarsat-2全极化SAR影像，从可视化效果和定量精度两个方面评价方法有效性。相关结果表明，本文方法优于单时相极化SAR超像素生成方法和现有的多时相极化SAR超像素方法，超像素能够有效贴合研究区域地块边界，是一种有效的超像素生成方法。

Superpixel generation is an important pre-processing step in the object-level data processing system, which is of great practical significance for the efficient processing and application of multi-temporal and multi-polarized SAR data. The single-temporal superpixel segmentation method does not fully utilize the complete scattering information of the segmented objects in the time series. To address this problem, this paper proposes a multi-temporal PolSAR Images adaptive cooperative segmentation method based on the Simple Linear Iterative Clustering(SLIC) model, which takes full use of the advantages of fully observed and describable time-varying characteristics of the time-series PolSAR data. Firstly, this method calculates the time-series PolSAR similarity distance based on Wishart distribution by uniting the polarization covariance matrix of multi-temporal; then uses multi-temporal polarization SAR data to perform gradient calculation to detect image edges; Finally, a homogeneity measure factor based on multi-temporal polarimetric SAR edge detection is proposed to adaptively balance the weight relationship between polarimetric distance and spatial distance. In this paper, we used 8 Radarsat-2 quad-polarization SAR images to evaluate the effectiveness of this method in terms of both visualization effect and quantitative accuracy. The results show that the method in this paper outperforms the single-temporal PolSAR superpixel generation method and the existing traditional multi-temporal PolSAR superpixel method. For example, as for the superpixel generation result with the quad-polarization SAR data (K = 12000), the value of the boundary recall(BR) and the achievable segmentation accuracy(ASA) by the proposed similarity measure and homogeneity factor is about 93.58% and 95.13%, respectively To address the problem that the single-temporal polarization SAR segmentation does not consider the time-varying characteristics of the ground object polarization characteristics, this paper proposes a multi-temporal polarization SAR image adaptive collaborative segmentation method based on the SLIC model. The experimental results show that compared with the segmentation method based on single-temporal data and the traditional multi-temporal polarimetric SAR superpixel segmentation method, the superpixels generated by this paper have obvious advantages in both visualization effect and quantitative accuracy, and can effectively fit the ground truth boundary, which proves that the proposed method is an effective superpixel generation method.