Abstract: Synthetic aperture Radar is a two- dimensional radar or three-dimensional radar that plays a prominent role in remote sensing. After many researchers have been done the advanced radar is polar metric synthetic aperture radar. PolSAR radar is fully polar metric radar it provides valuable data on relating experimental target than conventional artificial aperture radar. Therefore, it plays a significant job in numerous grounds like agriculture, military and geology. Based on this classification, ISODATA and minimum distance classification are utilized to explain the spatial and spectral data of polarimetric synthetic aperture radar. The initial step is to create the spatial data utilized for pre- processing stage. The PolSAR image is produced by decomposition and super pixels and then the date is estimated. The next method is to utilize spatial data for post-processing it sustains the minimum distance classification .The result is obtained by minimum distance categorization. The third method utilizes spatial data openly for categorization. In this method, SENTINEL data is applied to classify the PolSAR image. This SENTINEL data is classified by using SNAP software Based on Max-Margin the trademark work is structured and restrictive arbitrary field is utilized to proliferate the logical data in both naming field and perception field. Otherworldly term and spatial term are two significant pieces of the representation