Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
CoRR(2024)
摘要
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.
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