Abstract
The process of identifying change in remote sensing images has been a focal point of research for decades now. Many classical algorithms exist, and many new modern ones are still being developed. These algorithms can be divided into supervised and unsupervised. In this work an unsupervised method is presented. This method relies on the scene alignment algorithm SIFT flow. It is shown that building upon simple principles an accurate change map can be obtained from the SIFT descriptor flow of the two input images. Furthermore, it is shown that this method despite its simplicity exceeds other unsupervised methods and comes close to supervised ones, even exceeding them in some metrics. Lastly, the advantages of SIFT flow in comparison to the supervised methods are highlighted alongside its own downsides.
| Original language | English |
|---|---|
| Pages (from-to) | 47-52 |
| Number of pages | 6 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 46 |
| Issue number | M-2-2022 |
| DOIs | |
| Publication status | Published - 25 Jul 2022 |
| Externally published | Yes |
| Event | 2022 Annual Conference, ASPRS 2022 - Denver, United States Duration: 21 Mar 2022 → 25 Mar 2022 |
Bibliographical note
Publisher Copyright:© 2022 B. Awad.
Keywords
- Change detection
- Satellite imagery
- Scale invariant
- SIFT flow
- Unsupervised change detection.
- Unsupervised learning
- Very high resolution