A Comparative Study of Bag-of-Words and Bag-of-Topics Models of EO Image Patches

The large volume of detailed land cover features, provided by high resolution Earth observation (EO) images, has attracted considerable interest in the discovery of these features by learning systems. In this letter, we perform latent Dirichlet allocation on the bag of words (BoW) representation of collections of EO image patches to discover their semantic-level features, the so-called topics. To assess the discovered topics, the images are represented based on the occurrence of different topics, called bag of topics (BoT). The value added by BoT to the BoW model of image patches is then measured based on existing human annotations of the data. In our experiments, we compare the classification accuracy results of BoT and BoW representations of two different remote sensing image data sets, a multispectral optical data set and a synthetic-aperture-radar data set. Experimental results demonstrate that BoT can provide a compact and semantically meaningful representation of data; it either causes no significant reduction in the classification accuracy or increases the accuracy by a sufficient number of topics.