Semantic retrieval for remote sensing images using association rules mining

Since the properties of temporal and spatial complexity and mass diversity that remote sensing image data owns, remote sensing image retrieval becomes an international advanced frontier scientific issue in remote sensing. Content-based image retrieval technology is currently widely used; however, the difference between low-level features and high-level semantics, named semantic gap, becomes a difficult while important issue for remote sensing image retrieval. In this paper, a novel semantic retrieval method for remote sensing images using association rules mining is presented. Unlike the traditional content-based image retrieval methods, association rules are mined and used to express the semantic information of images instead of low-level features. The original image is firstly segmented into many objects; and then the classified association rules between the properties of objects are mined and transformed to semantic information by semantic annotation method; finally the semantic retrieval is achieved using the similarity measurement approach. The experimental results indicate that the proposed method can provide better retrieval performance than the existing content-based image retrieval methods.