Unsupervised Feature Learning for Aerial Scene Classification

The rich data provided by high-resolution satellite imagery allow us to directly model aerial scenes by understanding their spatial and structural patterns. While pixel- and object-based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper, we explore an unsupervised feature learning approach for scene classification. Dense low-level feature descriptors are extracted to characterize the local spatial patterns. These unlabeled feature measurements are exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We show that the statistics generated from the sparse features characterize the scene well producing excellent classification accuracy. We apply our technique to several challenging aerial scene data sets: ORNL-I data set consisting of 1-m spatial resolution satellite imagery with diverse sensor and scene characteristics representing five land-use categories, UCMERCED data set representing twenty one different aerial scene categories with sub-meter resolution, and ORNL-II data set for large-facility scene detection. Our results are highly promising and, on the UCMERCED data set we outperform the previous best results. We demonstrate that the proposed aerial scene classification method can be highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large facilities such as a shopping mall.