Deep hashing algorithms are widely exploited in retrieval tasks due to their low storage and retrieval efficiency. Most of them focus on global feature learning, while neglecting local fine-grained features and saliency information for drone images. In this article, we tackle these dilemmas with a novel deep saliency smoothing hashing (DSSH) algorithm, which can leverage saliency capture mechanism, distribution smoothing term, global features, and local fine-grained features to learn effective hash codes for drone image retrieval. The DSSH algorithm first designs an information extraction module to capture global features and local fine-grained features for drone images. Meanwhile, a saliency capture module is proposed to perform information interaction attention and visual enhancement attention, which can capture the saliency area of drone images effectively. On top of the two paths, a novel objective function is designed to preserve the similarity of hash codes, smooth the distribution of drone image datasets, and reduce the quantization errors between hash codes and hash-like codes concurrently. Extensive experiments on the Drone Action Dataset and ERA Drone Dataset demonstrate that the DSSH algorithm can further improve the retrieval performance compared with other deep hashing algorithms.