Remotely sensed image retrieval based on region-level semantic mining
1 Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, China
2 The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
3 Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
EURASIP Journal on Image and Video Processing 2012, 2012:4 doi:10.1186/1687-5281-2012-4Published: 23 March 2012
As satellite images are widely used in a large number of applications in recent years, content-based image retrieval technique has become important tools for image exploration and information mining; however, their performances are limited by the semantic gap between low-level features and high-level concepts. To narrow this semantic gap, a region-level semantic mining approach is proposed in this article. Because it is easier for users to understand image content by region, images are segmented into several parts using an improved segmentation algorithm, each with homogeneous spectral and textural characteristics, and then a uniform region-based representation for each image is built. Once the probabilistic relationship among image, region, and hidden semantic is constructed, the Expectation Maximization method can be applied to mine the hidden semantic. We implement this approach on a dataset consisting of thousands of satellite images and obtain a high retrieval precision, as demonstrated through experiments.