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
this https URL

遥感图像的自监督学习通过掩码自编码器（MAEs）近期吸引了广泛关注，该方法在内容基准图像检索（CBIR）方面具有重要潜力。然而，现有研究假设考虑的遥感图像是由单一图像传感器获取的，因此仅适用于单模态 CBIR 问题。本文首次探讨了 MAEs 在遥感领域跨传感器 CBIR 中的有效性，并针对多传感器遥感图像库提出了掩码图像建模的不同 CSMAE 模型，通过广泛的实验分析得出了利用掩码图像建模解决单模态和跨模态 CBIR 问题的指导方针。