Due to the low storage cost and fast query speed, the Hashing retrieval algorithm has been widely used for large scale image retrieval. Aiming at the inefficiency of large scale remote sensing image dataset training, a remote sensing image retrieval method based on query points for feature learning is proposed. First, the image features are extracted from the remote sensing image data training set with multiple semantic tags by using the deep convolutional network. Then, the Hashing function is learned from the query points and the Hashing codes of the query points are generated by using the learned Hashing function, and finally the binary Hashing codes of the whole image database are obtained through iterative learning, which is helpful to improve the retrieval accuracy. The feature extraction of the entire database is avoided in the process of image retrieval, and thus the supervised information in the large scale database is more effectively utilized for image retrieval. Extensive experimental results conducted on three different datasets demonstrate that the performance of the proposed method is better than those of several other state of the art approaches.