Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a significant potential for content-based image retrieval (CBIR) from ever-growing RS image archives. However, the existing studies on MAEs in RS assume that the considered RS images are acquired by a single image sensor, and thus are only suitable for uni-modal CBIR problems. The effectiveness of MAEs for cross-sensor CBIR, which aims to search semantically similar images across different image modalities, has not been explored yet. In this paper, we take the first step to explore the effectiveness of MAEs for sensor-agnostic CBIR in RS. To this end, we present a systematic overview on the possible adaptations of the vanilla MAE to exploit masked image modeling on multi-sensor RS image archives (denoted as cross-sensor masked autoencoders [CSMAEs]). Based on different adjustments applied to the vanilla MAE, we introduce different CSMAE models. We also provide an extensive experimental analysis of these CSMAE models. We finally derive a guideline to exploit masked image modeling for uni-modal and cross-modal CBIR problems in RS. The code of this work is publicly available at https://github.com/jakhac/CSMAE.

翻译：通过掩码自编码器（MAE）进行自监督学习近期在遥感（RS）图像表征学习中备受关注，因此对从不断增长的RS图像档案中开展基于内容的图像检索（CBIR）具有显著潜力。然而，现有关于RS中MAE的研究均假设所考虑的RS图像由单一图像传感器获取，因此仅适用于单模态CBIR问题。MAE在跨传感器CBIR（旨在跨不同图像模态检索语义相似图像）中的有效性尚未被探索。本文首次探索了MAE在RS中实现传感器无关CBIR的有效性。为此，我们系统梳理了原始MAE的可能改进方案，以利用多传感器RS图像档案中的掩码图像建模（称为跨传感器掩码自编码器[CSMAE]）。基于对原始MAE的不同调整，我们介绍了多种CSMAE模型，并对这些模型进行了广泛的实验分析。最终，我们提出了利用掩码图像建模解决RS中单模态与跨模态CBIR问题的指导原则。本工作代码已开源至https://github.com/jakhac/CSMAE。