  Segmentation is an essential step for remote sensing image processing. This
study aims to advance the application of the Segment Anything Model (SAM), an
innovative image segmentation model by Meta AI, in the field of remote sensing
image analysis. SAM is known for its exceptional generalization capabilities
and zero-shot learning, making it a promising approach to processing aerial and
orbital images from diverse geographical contexts. Our exploration involved
testing SAM across multi-scale datasets using various input prompts, such as
bounding boxes, individual points, and text descriptors. To enhance the model's
performance, we implemented a novel automated technique that combines a
text-prompt-derived general example with one-shot training. This adjustment
resulted in an improvement in accuracy, underscoring SAM's potential for
deployment in remote sensing imagery and reducing the need for manual
annotation. Despite the limitations encountered with lower spatial resolution
images, SAM exhibits promising adaptability to remote sensing data analysis. We
recommend future research to enhance the model's proficiency through
integration with supplementary fine-tuning techniques and other networks.
Furthermore, we provide the open-source code of our modifications on online
repositories, encouraging further and broader adaptations of SAM to the remote
sensing domain.
