  A vital and rapidly growing application, remote sensing offers vast yet
sparsely labeled, spatially aligned multimodal data; this makes self-supervised
learning algorithms invaluable. We present CROMA: a framework that combines
contrastive and reconstruction self-supervised objectives to learn rich
unimodal and multimodal representations. Our method separately encodes
masked-out multispectral optical and synthetic aperture radar samples --
aligned in space and time -- and performs cross-modal contrastive learning.
Another encoder fuses these sensors, producing joint multimodal encodings that
are used to predict the masked patches via a lightweight decoder. We show that
these objectives are complementary when leveraged on spatially aligned
multimodal data. We also introduce X- and 2D-ALiBi, which spatially biases our
cross- and self-attention matrices. These strategies improve representations
and allow our models to effectively extrapolate to images up to 17.6x larger at
test-time. CROMA outperforms the current SoTA multispectral model, evaluated
on: four classification benchmarks -- finetuning (avg. 1.8%), linear (avg.
2.4%) and nonlinear (avg. 1.4%) probing, kNN classification (avg. 3.5%), and
K-means clustering (avg. 8.4%); and three segmentation benchmarks (avg. 6.4%).
CROMA's rich, optionally multimodal representations can be widely leveraged
across remote sensing applications.
