With the joint advancement of numerous related fields of remote sensing, the amount of remote sensing data is growing exponentially. As an essential remote sensing Big Data management technique, content-based remote sensing image retrieval has attracted more and more attention. A novel deep attention hashing with distance-adaptive ranking (DAH) is proposed for remote sensing image retrieval in this article. First, a channel-spatial joint attention mechanism is employed for feature extraction of remote sensing images to make the proposed DAH method focus more on the critical details of the remote sensing images and suppress irrelevant regional responses. Second, a novel balanced pairwise weighted loss function is proposed to enable discrete hash codes to participate in neural network training, which contains pairwise weighted similarity loss, classification loss, and quantization loss. The pairwise weighted similarity loss is designed to decrease the impact of the imbalance of positive and negative sample pairs. The classification loss and quantization loss are added to the loss function to decrease background interference and information loss during the quantization phase, respectively. Finally, a distance-adaptive ranking strategy with category-weighted Hamming distance is presented in the retrieval phase to utilize the category probability information fully. Experiments on benchmark datasets compared with state-of-the-art methods demonstrate the effectiveness of the proposed DAH method.