Abstract
This paper proposes a manifold based approach for change detection in multitemporal hyperspectral images. Manifold representation, using Laplacian Eigenmaps, is applied for dimensionality reduction on stacked temporal datasets and change detection on the reduced datasets. The resulting latent vectors are utilized to cluster the changed vs. unchanged regions. A semi-supervised scheme is also proposed which circumvents the challenging thresholding issue and enables satisfactory binary change detection outputs. The proposed approach is validated on two real bitemporal hyperspectral datasets.
| Original language | English |
|---|---|
| Title of host publication | 2021 11th Workshop on Hyperspectral Imaging and Signal Processing |
| Subtitle of host publication | Evolution in Remote Sensing, WHISPERS 2021 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781665436014 |
| DOIs | |
| Publication status | Published - 24 Mar 2021 |
| Externally published | Yes |
| Event | 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021 - Amsterdam, Netherlands Duration: 24 Mar 2021 → 26 Mar 2021 |
Publication series
| Name | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing |
|---|---|
| Volume | 2021-March |
| ISSN (Print) | 2158-6276 |
Conference
| Conference | 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021 |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 24/03/21 → 26/03/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Change detection
- hyperspectral
- Laplacian Eigenmaps
- manifold