The text elucidates the application of Synthetic Aperture Radar (SAR) in remote sensing, highlighting its advantages over optical imaging, such as its ability to operate effectively regardless of lighting and weather conditions. It discusses the recent integration of deep convolutional neural networks (DCNNs) in SAR image interpretation, emphasizing the challenges posed by the lack of labeled datasets and the unique characteristics of SAR data, which differ significantly from optical data. The Clarifai platform is introduced as a tool for SAR imagery classification, allowing users to test and compare multiple classification schemes efficiently. Utilizing transfer learning, Clarifai enables the adaptation of models trained on optical images to SAR datasets like MSTAR10 and TenGeoP-SARwv, with varying success. The platform simplifies deep model training from scratch, facilitating the exploration of different neural network architectures for improved SAR data analysis. The text concludes by underscoring the continued potential for deep learning in SAR and Clarifai's commitment to expanding its repository of SAR datasets and pre-trained models to support ongoing research advancements.