In the era of constantly increasing Earth Observation (EO) data collections, information extraction and data analysis should be enhanced with a multi-temporal component enabled by the temporal resolution of satellite missions and create handy, yet powerful tools for those applications involving monitoring of land cover. The image time series, as results of the satellite revisiting period, gives you insights not only on a certain area, but also on its representation at different moments of time. In order to limit the issues that might arise due to irregular time sampling of multispectral data, the authors propose a Synthetic Aperture Radar (SAR) image time series for analysis. To this point, the main goal is to mine the satellite image time series (SITS) for understanding the temporal behaviour of an area in terms of evolution and persistency. The paper introduces an analytical approach, combining coherent and no coherent analysis of SAR SITS content. We propose the Latent Dirichlet Allocation model to extract categories of evolution from the SAR SITS and techniques which study statistical and coherent proprieties of the targets to identify the structures with stable electromagnetic characteristics over time, named Persistent Scatterers (PS). The obtained results indicate an evolutionary character hidden inside the persistent class. The results obtained on 30 ERS images encourages further analysis on Sentinel 1 data.