Transfer Learning with Pretrained Remote Sensing Transformers
This work addresses the challenge of model robustness for remote sensing applications under distribution shifts, but it is incremental as it builds on existing pretraining methods and focuses on specific domain improvements.
The paper tackled the problem of how pretrained remote sensing transformers perform under distribution shifts by pretraining SatViT-V2 on 1.3 million satellite images and testing it across 14 biomes, finding it outperformed SatViT-V1 by 3.1% in-distribution and 2.8% out-of-distribution, with LPFT further improving performance by 1.2% and 2.4%, respectively.
Although the remote sensing (RS) community has begun to pretrain transformers (intended to be fine-tuned on RS tasks), it is unclear how these models perform under distribution shifts. Here, we pretrain a new RS transformer--called SatViT-V2--on 1.3 million satellite-derived RS images, then fine-tune it (along with five other models) to investigate how it performs on distributions not seen during training. We split an expertly labeled land cover dataset into 14 datasets based on source biome. We train each model on each biome separately and test them on all other biomes. In all, this amounts to 1638 biome transfer experiments. After fine-tuning, we find that SatViT-V2 outperforms SatViT-V1 by 3.1% on in-distribution (matching biomes) and 2.8% on out-of-distribution (mismatching biomes) data. Additionally, we find that initializing fine-tuning from the linear probed solution (i.e., leveraging LPFT [1]) improves SatViT-V2's performance by another 1.2% on in-distribution and 2.4% on out-of-distribution data. Next, we find that pretrained RS transformers are better calibrated under distribution shifts than non-pretrained models and leveraging LPFT results in further improvements in model calibration. Lastly, we find that five measures of distribution shift are moderately correlated with biome transfer performance. We share code and pretrained model weights. (https://github.com/antofuller/SatViT)