Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images
PLA Information Engineering University · Nanjing University of Information Science and Technology · +1 more institution
Abstract
Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety of visual scenes. However, their direct use in many Remote Sensing (RS) applications is often unsatisfactory due to the special imaging properties of RS images. In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve change detection (CD) in very high-resolution (VHR) remote sensing images (RSIs). We employ the visual encoder of FastSAM, a variant of the SAM, to extract visual representations in RS scenes. To adapt FastSAM to focus on some specific ground objects in RS scenes, we propose a…
Citation impact
- FWCI
- 44.91
- Percentile
- 100%
- References
- 54
Authors
6Topics & keywords
- Remote sensing
- Change detection
- Computer science
- Artificial intelligence
- Computer vision
- Geology