Retrieval and management of the vast quantitative image data needs efficient approaches of content-based image retrieval. Current image retrieval methods in geographic image databases use only one kind of image features

which can not describe image content completely. In this paper

a remote sensing image retrieval approach using color and texture fused features is presented. A given image is decomposed using quin-tree

and each subimage except the center one is separated into 5 sublevel subimages until the size of the subimage equals to or is great than 16×16 pixels. The energy values of the image are calculated via multi-channel Gabor filter

and the mean values and standard deviations of each subimage are extracted as texture features. The color features are calculated too. Similarity between a given query image and database subimages

which sizes are approximately equal to the former

are measured using linear weighted distance of color and texture features. Then the top similar subimages are returned as query results. This approach is applied to retrieve high resolution remote sensing images from database

and its efficiency is confirmed by the experiments. The comparison of precision and recall between texture-based and color -texture-based image retrieval shows that synthesized features describe image content better than lonely one does.