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A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery

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dc.contributor.author Salmon, BP
dc.contributor.author Kleynhans, W
dc.contributor.author Olivier, JC
dc.contributor.author Schwegmann, CP
dc.contributor.author Olding, WC
dc.date.accessioned 2016-05-16T10:16:42Z
dc.date.available 2016-05-16T10:16:42Z
dc.date.issued 2015-07
dc.identifier.citation Salmon BP, Kleynhans W, Olivier JC, Schwegmann CP, Olding WC. 2015. A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015, pp 4372-4375. en_US
dc.identifier.issn 2153-6996
dc.identifier.uri http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7326795
dc.identifier.uri http://hdl.handle.net/10204/8538
dc.description 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website en_US
dc.description.abstract In this paper the authors 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. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Workflow;15623
dc.subject Context awareness en_US
dc.subject Graphical Models en_US
dc.subject Image classification en_US
dc.subject Remote Sensing en_US
dc.subject Satellites en_US
dc.subject Statistics en_US
dc.title A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery en_US
dc.type Conference Presentation en_US


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