A new neural network-based approach for automatic annotation of remote sensing imagery

In this paper, we propose a novel model for automatic annotation of high-resolution Earth Observation (EO) overhead imagery, entirely based on neural networks. The model combines the unsupervised pattern recognition of Self-Organizing Map (SOM) with the supervised classifier of Concurrent SOMs (CSOM). The performances of the proposed method is compared with those of the annotation based on classical statistical techniques of Latent Dirichlet Allocation (LDA) and K-Means. The experiments prove the effectiveness of the proposed method.