<p>The advent of hyperspectral imaging has made it possible to do fine-grained grouping and classification in a variety of contexts, making it an invaluable tool for comprehensive scene analysis. Feature extraction in traditional methods is either done by hand or using deep learning, both of which require huge labelled datasets and a lot of computing power. We offer a new framework called Smart Pixels: Interpretable Active Dictionary Learning with Spatial Coherence Regularization for Hyperspectral Image Classification. Our active dictionary learning methods can minimize annotation costs while iteratively refining representations of discriminative features. We propose a joint learning model, where the feature learning and classification goals are maximized at the same time while incorporating spatial coherence, dictionary diversity, and class-atom associations for improved interpretability. By utilizing active learning concepts, we create an optimization problem that iteratively selects the most informative samples for dictionary parameter refinement through a combination of uncertainty metrics and spatial representativeness scores. By utilizing an Alternating Direction Method of Multipliers (ADMM) framework, the optimization is solved quite efficiently. Our strategy outperforms state-of-the-art classification techniques in exhaustive experiments conducted on four benchmark hyperspectral datasets: Indian Pines, Pavia University, Salinas, and Botswana. We present Smart Pixels method that is ideal for large-scale hyperspectral data analysis since it improves accuracy while simultaneously decreasing annotation efforts while providing enhanced explainability through various visualization and interpretation techniques.</p>