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Synthetic Aperture Radar is an interesting topic of research for scientists & researchers as it is associated with polarimetric information which helps to detect surface & subsurface features of land, sea, and ice. Classical techniques include the use of polarimetric information to simplify SAR image interpretation and to classify it for various earth observation applications. The deep learning (DL) techniques like Convolutional Neural Network (CNN), extract useful information from an image (here dual polarimetric SAR dataset) about the land surface to segment or classify the dataset for various earth applications. In the current research paper convolutional neural network is used to automatically classify RISAT-1 dataset over the Mumbai region for land cover classification. Also impact of patch size variation was studied. In addition, the efficiency of the CNN model was tested using an approach similar to transfer learning …