S4: Self-Supervised Sensing Across the Spectrum
This addresses the problem of data scarcity for researchers and practitioners in environmental monitoring and agriculture, offering an incremental improvement over existing methods.
The paper tackles the challenge of limited labeled data for satellite image time series segmentation by proposing S4, a self-supervised pre-training approach that reduces the need for annotations, achieving competitive performance on multiple datasets with limited labeled data.
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.