Ongoing research at Los Alamos National Laboratory studies the Earth's radio frequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. Such impulsive events occur in the presence of additive noise and structured clutter and appear as broadband nonlinear chirps at a receiver on-orbit due to ionospheric dispersion. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich RF lightning database. Application of modern pattern recognition techniques to this database may further lightning research and potentially improve event discrimination capabilities for future satellite payloads. We extend two established dictionary learning algorithms, K-SVD and Hebbian, for use in classification of satellite RF data. Both algorithms allow us to learn features without relying on analytical constraints or additional knowledge about the expected signal characteristics. We use a pursuit search over the learned dictionaries to generate sparse classification features and discuss performance in terms of event classification using a nearest subspace classifier. We show a use of the two dictionary types in a mixed implementation to showcase algorithm distinctions in extracting discriminative information. We use principal component analysis to analyze and compare the learned dictionary spaces to the real data space, and we discuss some aspects of computational complexity and implementation. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.