High resolution Remote Sensing Image retrieval Based on Multi-visual Feature and K-centroid Clustering
Abstract
I 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 databasefor the color <b1 6>, shape andTexture
features of the remote sensing image respectively , the improved k-centroid clustering algorithm which firstly
clustered the images in the remote Sens ing 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.
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