S4: Self-Supervised Sensing Across the Spectrum
arxiv(2024)
摘要
Satellite image time series (SITS) segmentation is crucial for many
applications like environmental monitoring, land cover mapping and agricultural
crop type classification. However, training models for SITS segmentation
remains a challenging task due to the lack of abundant training data, which
requires fine grained annotation. We propose S4 a new self-supervised
pre-training approach that significantly reduces the requirement for labeled
training data by utilizing two new insights: (a) Satellites capture images in
different parts of the spectrum such as radio frequencies, and visible
frequencies. (b) Satellite imagery is geo-registered allowing for fine-grained
spatial alignment. We use these insights to formulate pre-training tasks in S4.
We also curate m2s2-SITS, a large-scale dataset of unlabeled,
spatially-aligned, multi-modal and geographic specific SITS that serves as
representative pre-training data for S4. Finally, we evaluate S4 on multiple
SITS segmentation datasets and demonstrate its efficacy against competing
baselines while using limited labeled data.
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