High resolution Remote Sensing Image retrieval Based on Multi-visual Feature and K-centroid Clustering

At present , Resolution remote Sensing image retrieval based on single content has the problem of one-sided description and imprecise information. The color, shape and Texture features of remote sensing images were fully used and combined to form multi-vision remote sensing image retrieval in order to solve this problem. Through a series of iterative operations, the best proportionality coefficient for this three features to treat Types of remote sensing images can be obtained, which gets a better search result. Aiming at the problem of the retrieval speed are slow when searching the large image database for the color , shape and Texture features of the remote sensing image respectively , the improved k-centroid clustering algorithm which firstly clustered the images in the remote Sensing image database is introduced to reduce the retrieval scope as as the improve the retrieval speed. The experimental results show that this method has the retrieval results.