Large-scale "foundation models" have gained traction as a way to leverage the
vast amounts of unlabeled remote sensing data collected every day. However, due
to the multiplicity of Earth Observation satellites, these models should learn
"sensor agnostic" representations, that generalize across sensor
characteristics with minimal fine-tuning. This is complicated by data
availability, as low-resolution imagery, such as Sentinel-2 and Landsat-8 data,
are available in large amounts, while very high-resolution aerial or satellite
data is less common. To tackle these challenges, we introduce cross-sensor
self-supervised training and alignment for remote sensing (X-STARS). We design
a self-supervised training loss, the Multi-Sensor Alignment Dense loss (MSAD),
to align representations across sensors, even with vastly different
resolutions. Our X-STARS can be applied to train models from scratch, or to
adapt large models pretrained on e.g low-resolution EO data to new
high-resolution sensors, in a continual pretraining framework. We collect and
release MSC-France, a new multi-sensor dataset, on which we train our X-STARS
models, then evaluated on seven downstream classification and segmentation
tasks. We demonstrate that X-STARS outperforms the state-of-the-art by a
significant margin with less data across various conditions of data
availability and resolutions.

我们介绍了 “X-STARS” 的远程感知数据的跨传感器自监督训练和对齐方法，通过设计多传感器对齐稠密损失（MSAD）来对齐不同分辨率的传感器所表示的数据，从而提高模型在各种数据可用性和分辨率条件下的性能。