RingMo-SAM: A Foundation Model for Segment Anything in Multimodal Remote-Sensing Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 16
• Main Authors: Yan, Zhiyuan, Li, Junxi, Li, Xuexue, Zhou, Ruixue, Zhang, Wenkai, Feng, Yingchao, Diao, Wenhui, Fu, Kun, Sun, Xian
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Datasets; Decoupling; Feature extraction; Image processing; Image segmentation; Multimodal remote-sensing images; prompt learning; Radar imaging; Radar polarimetry; Remote sensing; SAR (radar); segment anything model (SAM); Semantic segmentation; Synthetic aperture radar; Task analysis; Training

The proposal of the segment anything model (SAM) has created a new paradigm for the deep-learning-based semantic segmentation field and has shown amazing generalization performance. However, we find it may fail or perform poorly on multimodal remote-sensing scenarios, especially synthetic aperture r...