Image retrieval is a key technology for data acquisition and knowledge transformation under the background of remote sensing data. Remote sensing images with high spatial resolution provide Object details and diverse structures of land features

thereby making differences in local visual feature as evident. Most existing retrieval methods represent and model image contents on the basis of low-level visual features of the image

leading to limited retrieval performance of high-resolution remote sensing image. This paper presents a novel retrieval method for high-spatial resolution remote sensing image; the proposed method utilizes abundant information about the spatial distribution and structure of land features. In the proposed method

data on low-level visual features and land-feature relations are utilized to represent the content of remote sensing images. Firstly

Quin+ tree is used to decompose the original large-sized image into a feature block sequence with a fixed size. Lowlevel visual features and land-feature relation descriptions are then extracted from the corresponding feature block. Feature histograms for candidate blocks are constructed according to the descriptions of the feature blocks. In each feature block

low-level visual information is represented using color and texture histograms. Moreover

land-feature spatial relation information is modeled as Object–direction and category co-occurrence histograms. Finally

the similarity between the query template and all candidate blocks is determined according to the feature histograms. Candidate blocks with high similarity values are selected as the final retrieval results. Several high-resolution remote sensing images of Quick Bird and ZY-3 are used in the experiments to confirm the effectiveness of the proposed method. Based on the retrieval results of the proposed method

the average retrieval precision of water

farmland

buildings

and other categories are higher than 0.75. In addition

the proposed method is compared with two typical CBIR methods. Quantitative evaluation indicates that the proposed method yields optimal results. The proposed method can significantly improve the retrieval performance because it considers the description of space relationship information among different land features.