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A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery
Citation
Salmon, BP and Kleynhans, W and Olivier, JC and Schwegmann, CP and Olding, WC, A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 26-31 July 2015, Milan, Italy, pp. 4372-4375. 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.2015.7326795
Abstract
In this paper we present a 2-tier higher order Conditional Random
Field which is used for land cover classification. The
Conditional Random Field is based on probabilistic messages
being passed along a graph to compute efficiently the conditional
probability for a land cover class. Conventionally the
information is passed among direct spatial neighbors to improve
classification accuracy. The inclusion of higher order
descriptive structures in the graphs allow for more information
to be pass along to further improve classification accuracy.
Unfortunately this increases the computational cost beyond
what is feasible to classify a large geographical area. In
this work we investigate a spatially based cluster potential to
improve classification accuracy while keeping the computational
costs tractable. We also expand the typical 1-tier protograph
used in conventional CRFs to a 2-tier graph to encapsulate
the temporal dimension. This further improves the classification
accuracy by modeling the seasonal variations experienced
throughout the year. The conventional and higher order
CRF are compared to a Random Forest on monthly composited
Landsat images. These two CRFs are then compared to
the same CRFs expanded to a 2-tier graph. An overall improvement
between 2-4% is observed in our study area which
is located near the city of Vryheid, South Africa.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | context awareness, graphical models, image classification, remote sensing, satellites, statistics |
| 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 |
| UTAS Author: | Salmon, BP (Dr Brian Salmon) |
| UTAS Author: | Olivier, JC (Professor JC Olivier) |
| UTAS Author: | Olding, WC (Mr Willem Olding) |
| ID Code: | 103119 |
| Year Published: | 2015 |
| Deposited By: | Engineering |
| Deposited On: | 2015-09-22 |
| Last Modified: | 2016-08-02 |
| Downloads: | 0 |
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