Remote-sensing image retrieval by combining image visual and semantic features

• Published in: International journal of remote sensing Vol. 34; no. 12; pp. 4200 - 4223
• Main Authors: Wang, M, Wan, Q.M, Gu, L.B, Song, T.Y
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 2013
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; color; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; image analysis; Internal geophysics; On-line systems; Online; Remote sensing; Retrieval; Semantics; Support vector machines; Teledetection and vegetation maps; Texture; Visual; color; textures; object oriented; fine-grained materials; Spatial orientation; System design; remote sensing; Support vector machine; classification; Image
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2013.774098

This article presents a remote-sensing image retrieval scheme using image visual, object, and spatial relationship semantic features. It includes two main stages, namely offline multi-feature extraction and online query. In the offline stage, remote-sensing images are decomposed into several blocks using the Quin-tree structure. Image visual features, including textures and colours, are extracted and stored. Further, object-oriented support vector machine (SVM) classification is carried out to obtain the image object semantic. A spatial relationship semantic is then obtained by a new spatial orientation description method. The online query stage, meanwhile, is a coarse-to-fine process that includes two sub-steps, which are a rough image retrieval based on the object semantic and a template-based fine image retrieval involving both visual and semantic features. This method is different from many other semantic-based remote-sensing image retrieval methods and is suitable for ‘scene matching’. Moreover, the scheme is distinctive in system design, spatial relationship semantic description, and method of combining and utilizing visual and semantic features.