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In recent years, the rapid development of remote sensing (RS) technology has led to a drastic increase in the availability of RS images. This calls for the need to develop new methods able to effectively and efficiently retrieve the required instances from a massive amount of RS imagery. In retrieval tasks, finding the nearest-neighbor sample of the retrieval query is a fundamental research topic. Exhaustive comparison is the simplest method to accomplish this task. However, due to the involved computational complexity and memory limitations, this solution is no longer feasible in large data retrieval tasks. As an important branch of approximate nearest-neighbor retrieval (NNR), hash algorithms transform high-dimensional data into low-bit expressions (hash codes) with elements of 0 and 1 to reduce storage and computational costs. Hash algorithms aim to preserve the same nearest-neighbor relationship between the learned hash codes and the original data. Existing hash algorithms are divided into two classes: shallow and deep methods. Furthermore, deep hash algorithms can be divided into (semi-) supervised and unsupervised algorithms. In this article, representative hash-based RS image retrieval (HBRSIR) methods are reviewed, studying the application of hashing in other areas of the RS community and introducing available datasets and evaluation metrics for RS image retrieval (RSIR). The performance of representative and cross-modal hashing methods is validated using two common RSIR datasets (UCMerced and AID) and a cross-modal dataset (DSRSID). Prospects of future work summarizing HBRSIR are also provided.