A method for remote sensing image retrieval based on convolutional neural networks was proposed. First, the convolution and pooling of remote sensing images were conducted by multi-layer convolutional neural networks. The feature maps of each image were obtained, and the high-level features were extracted to build the image feature database. In this process, the training of networks parameters and the Softmax classifier were completed using feature maps. Then, in the image retrieval stage, classification was introduced by the softmax classifier which will improve the accuracy of image retrieval. Lastly, the remote sensing image retrieval was sorted based on the similarity between the query image and database. Retrieval experiments were performed on the high-resolution optical remote sensing images. The average retrieval precision on five kinds including water, plant, building, farmland and land is 98.4%, and the retrieval precision on seven types (adding plane and ship) is 959%. The introduction of class information improves the retrieval precision and speed, saving time by 17.6% approximately. The proposed method behaves better than the methods that based on color feature, texture feature and the bag of words model, and the results show that the high-level feature from deep convolutional neural networks can represent image content effectively. Experimeat indicates that retrieval speed and accuracy of optical remote-sensing images can be effectively increased in this method.