Skip to Main Content
In this letter, we are interested in the annotation of large satellite images, using semantic concepts defined by the user. This annotation task combines a step of supervised classification of patches of the large image and the integration of the spatial information between these patches. Given a training set of images for each concept, learning is based on the latent Dirichlet allocation (LDA) model. This hierarchical model represents each item of a collection as a random mixture of latent topics, where each topic is characterized by a distribution over words. The LDA-based image representation is obtained using simple features extracted from image words. We then exploit the capability of the LDA model to assign probabilities to unseen images, in order to classify the patches of the large image into the semantic concepts, using the maximum-likelihood method. We conduct experiments on panchromatic QuickBird images with 60-cm resolution. Taking into account the spatial information between the patches shows to improve the annotation performance.