The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot

By Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, Jonathan Li, José Marcato Junior. International Journal of Applied Earth Observation and Geoinformation, 2023

408 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/fb4ed7d3f24c69d33dc0654cf91ed8409f8dafb3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Keyan Chen
2. Zhenwei Shi
3. Chenyang Liu
4. Wenyuan Li
5. Hao Chen
6. Haotian Zhang
7. Zhengxia Zou
8. Simone Scardapane

In Media Technology

1. Keyan Chen
2. Zhenwei Shi
3. Wenyuan Li
4. Zhengxia Zou
5. Simone Scardapane
6. Valerio Marsocci
7. Sheng Fang
8. Zhe Li

Citations in the corpus

• RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation Based on Visual Foundation Model (2023, background): “This advancement opens new horizons for intel-1558-0644 ligent image analysis and understanding [35], [36], [37], [38], [39], [40], [41].”