SAM-Assisted Remote Sensing Imagery Semantic Segmentation With Object and Boundary Constraints
Chinese University of Hong Kong, Shenzhen · Wuhan University of Science and Technology · +1 more institution
Abstract
Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise information for diverse downstream applications. Recent development of the segment anything model (SAM), an advanced general-purpose segmentation model, has revolutionized this field, presenting new avenues for accurate and efficient segmentation. However, SAM is limited to generating segmentation results without class information. Meanwhile, the segmentation map predicted by current methods generally exhibits excessive fragmentation and inaccuracy of boundary. This article introduces a streamlined framework designed to leverage the raw output of SAM by exploiting two novel concepts called SAM-generated object (SGO) and…
Citation impact
- FWCI
- 32.56
- Percentile
- 100%
- References
- 85
Authors
6Topics & keywords
- Computer science
- Segmentation
- Remote sensing
- Computer vision
- Artificial intelligence
- Image segmentation
- Boundary (topology)
- Object (grammar)