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A Markov Random Field model for decision level fusion of multi source image segments
Citation
Olding, WC and Olivier, JC and Salmon, BP, A Markov Random Field model for decision level fusion of multi source image segments, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 26-31 July 2015, Milan, Italy, pp. 2385-2388. ISBN 978-1-4799-7929-5 (2015) [Refereed Conference Paper]
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Copyright Statement
Copyright 2015 IEEE
Official URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp...
DOI: doi:10.1109/IGARSS.2015.7326289
Abstract
We present a method based on Markov Random Fields
(MRFs) for conducting decision level fusion of segments
derived from multiple images of the same region. These
images do not have to share the same resolution or sensor
characteristics. By working at the segment level we preserve
the advantages of segment based image classification
while also incorporating the benefits of using multiple image
sources. This is achieved through including an edge potential
that connects overlapping segments from different images
and encourages them to share the same label based on their
degree of overlap. Experimentation on the fusion of Landsat
and SPOT5 imagery indicates that this method can deliver
improved accuracy when classifying types of forest.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | graphical models, image classification, Markov Random Fields, sensor fusion, vegetation mapping |
| Research Division: | Engineering |
| Research Group: | Geomatic Engineering |
| Research Field: | Photogrammetry and Remote Sensing |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding Knowledge |
| Objective Field: | Expanding Knowledge in Engineering |
| Author: | Olding, WC (Mr Willem Olding) |
| Author: | Olivier, JC (Professor JC Olivier) |
| Author: | Salmon, BP (Dr Brian Salmon) |
| ID Code: | 103118 |
| Year Published: | 2015 |
| Deposited By: | Engineering |
| Deposited On: | 2015-09-22 |
| Last Modified: | 2016-08-02 |
| Downloads: | 0 |
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