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.

中文翻译：

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基于哈希的遥感图像检索


近年来，遥感（RS）技术的快速发展导致遥感图像的可用性急剧增加。这就需要开发新的方法，能够有效且高效地从大量遥感图像中检索所需的实例。在检索任务中，找到检索查询的最近邻样本是一个基础研究课题。详尽的比较是完成此任务的最简单方法。然而，由于涉及的计算复杂性和内存限制，该解决方案在大型数据检索任务中不再可行。哈希算法作为近似最近邻检索（NNR）的一个重要分支，将高维数据转换为元素为0和1的低位表达式（哈希码），以减少存储和计算成本。哈希算法的目的是在学习的哈希码和原始数据之间保留相同的最近邻关系。现有的哈希算法分为两类：浅层方法和深层方法。此外，深度哈希算法可以分为（半）监督算法和无监督算法。本文回顾了代表性的基于哈希的 RS 图像检索 (HBRSIR) 方法，研究了哈希在 RS 社区其他领域的应用，并介绍了 RS 图像检索 (RSIR) 的可用数据集和评估指标。使用两个常见的 RSIR 数据集（UCMerced 和 AID）和跨模态数据集 (DSRSID) 验证代表性和跨模态哈希方法的性能。还提供了总结 HBRSIR 的未来工作的展望。