  We explore the "hidden" ability of large-scale pre-trained image generation
models, such as Stable Diffusion and Imagen, in non-visible light domains,
taking Synthetic Aperture Radar (SAR) data for a case study. Due to the
inherent challenges in capturing satellite data, acquiring ample SAR training
samples is infeasible. For instance, for a particular category of ship in the
open sea, we can collect only few-shot SAR images which are too limited to
derive effective ship recognition models. If large-scale models pre-trained
with regular images can be adapted to generating novel SAR images, the problem
is solved. In preliminary study, we found that fine-tuning these models with
few-shot SAR images is not working, as the models can not capture the two
primary differences between SAR and regular images: structure and modality. To
address this, we propose a 2-stage low-rank adaptation method, and we call it
2LoRA. In the first stage, the model is adapted using aerial-view regular image
data (whose structure matches SAR), followed by the second stage where the base
model from the first stage is further adapted using SAR modality data.
Particularly in the second stage, we introduce a novel prototype LoRA (pLoRA),
as an improved version of 2LoRA, to resolve the class imbalance problem in SAR
datasets. For evaluation, we employ the resulting generation model to
synthesize additional SAR data. This augmentation, when integrated into the
training process of SAR classification as well as segmentation models, yields
notably improved performance for minor classes
