Feature Analysis in Satellite Image Classification Using LC-KSVD and Frozen Dictionary Learning

Feature ranking is an interesting problem in data science due to the time and effort wasted on collecting, storing, and processing redundant features. This may also lead to over-fitted and under-trained machine learning (ML) and deep learning models. Although there are several feature ranking algorithms available, they lack an intuitive interpretation of the effect on the final ML model behavior. In this paper, we propose simple and intuitive feature ranking metrics based on sparse representation methods to be used in classification tasks. Sparse representation is an emerging image processing tool that can be effectively used in satellite/airborne image scene classification tasks. This paper applies two sparse representation methods, LCKSVD and Frozen Dictionary Learning, on handcrafted features taken from the Sat-4 and Sat-6 datasets as a preliminary test. Even though these methods report lower classification accuracies than state-of-art deep learning methods, they provide an intuitive understanding of the system model. Furthermore, sparse representation allows for the use of simpler linear classifiers in the classification stage to achieve relatively high performance. Finally, we present an analysis of the relationship between the learned sparse coefficients and the original feature space to explain the intuitive behavior of this model.