The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in a CRISM image. The pipeline leverages a highly discriminative representation learned through the use of Generative Adversarial Networks, such that in this novel representation space simple distance metrics are sufficient to discriminate between even very similar spectral shapes. The pipeline leverages this enhanced feature space to set up an open set classification problem that labels each new pixel as either a member of a known mineral class or novel spectral shape (or outliers). Following this, a segmentation technique is used on the outliers to group them, and further, reduce them to a representative set of the novel spectral shapes present in the image. These novel spectral shapes can then be labeled based on expert analysis and used to update the open-set classifier. The performance of these tools are validated over a subset of CRISM images from different parts of the Martian surface such as Jezero Crater, North East Syrtis, and Mawrth Vallis.