Measuring the semantic gap based on a communication channel model

The collected Earth Observation (EO) data volumes are increasing immensely. In the meantime, the need for retrieval of focused information for decision making is increasing. Due to the particular nature of EO sensors, recording signals very differently than humans perceptual system, the challenges raised by the semantic and sensory gaps are immensely amplified in designing retrieval methods for EO images. This article introduces a method based on communication channel model to quantify and measure the semantic gap, used to assess various feature descriptors for semantic annotation purposes. The approach uses Latent Dirichlet Allocation (LDA), considering images as the source and the semantic topics as the receiver. The parameters of LDA are estimated for computing the Mutual Information to assess latent semantics of feature space. We further introduce a method to measure the distance between humans´ and computer´s semantics. The results are validated using an SVM-based classifier for an annotated dataset.