Name
Abstract | ||
|---|---|---|
In this study, we have developed an approach for fusion of optical and SAR data for snow cover fraction (SCF) retrieval that avoids the typical blending effects when combining independently retrieved geophysical data from different sensors. Instead of undertaking the sensor fusion at the geophysical parameter level, the fusion is done at the electromagnetic signal level. A state model, based on hidden Markov model theory, has been developed for the simultaneous signal from the optical and the SAR sensors. The model goes through a given set of states through the snowmelt season where transition probability distribution functions of time have been determined for each state transition. A coupling between corresponding models for optical and SAR observations has been developed in order to make a more reliable model of the sensor co-variation. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IGARSS.2006.561 | international geoscience and remote sensing symposium |
Keywords | Field | DocType |
hidden Markov models,hydrological techniques,remote sensing,sensor fusion,snow,synthetic aperture radar,SAR data,data fusion,hidden Markov model,multisensor harmonization,optical data,snow cover area mapping | Coupling,Computer science,Synthetic aperture radar,Remote sensing,Fusion,Sensor fusion,Probability distribution,Snowmelt,Hidden Markov model,Snow | Conference |
ISSN | ISBN | Citations |
2153-6996 | 0-7803-9510-7 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rune Solberg | 1 | 21 | 5.26 |
| Hans Koren | 2 | 5 | 0.98 |
| Eirik Malnes | 3 | 32 | 10.30 |
| Haarpaintner, J. | 4 | 0 | 0.34 |