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arXiv Computer Vision By Yuki Natsume, Sang-Ho Yun, Chai Kiat Yeo

Data-Efficient ConvNeXt Pretraining for High-Resolution Sentinel-1 GRD SAR Representation Learning

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The paper introduces SparK-SAR, a sparse convolutional masked pretraining framework that leverages ConvNeXt for high‑resolution Sentinel‑1 GRD SAR data. It presents three key contributions: a domain‑adapted ConvNeXt‑RS encoder with a reduced stem stride, a lightweight ConvNeXt‑512 decoder that cuts VRAM usage while matching UNet accuracy, and an analysis of FP16 instability and layer‑wise learning‑rate decay benefits. Experiments on four Copernicus‑Bench S1GRD tasks show that the 27.8 M‑parameter ConvNeXt‑RS encoder outperforms a larger ViT baseline on most tasks and demonstrates strong data efficiency, with classification saturating at 10–25 % of the SSL4EO‑S12 corpus.

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