Remote sensing images have a large number of semantic objects, and the visual differences of the same semantic objects are large. Aiming at the problem that the global features extracted by convolutional neural network (CNN) cannot accurately describe the image content, a remote sensing image retrieval method based on regional attention mechanism is proposed. First, the fully connected layer of the CNN is removed, and the deep features are used as the input of regional attention network. Then, the CNN and regional attention network are trained respectively on remote sensing image dataset. After that, local image features with attention can be extracted. Finally, a multi-distance similarity metric matrix is constructed, and extended query is used to improve retrieval performance. Experimental results show that, compared with remote sensing image retrieval method based on global features, this method can effectively suppress the background of remote sensing images and unrelated image regions, and the retrieval performance is better on the two large remote sensing experimental data sets.