AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE
| Autor: | Albert, L., Rottensteiner, F., Heipke, C., Christophe, S., Raimond, A.-M., Yang, M., Coltekin, A., Mallet, C., Dowman, I., Paparoditis, N., Bredif, M., Oude Elberink, S. |
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| Rok vydání: | 2015 |
| Předmět: |
lcsh:Applied optics. Photonics
Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften Conditional random field Computer science Inference Context (language use) Land cover computer.software_genre lcsh:Technology Conditional Random Fields ddc:550 Layer (object-oriented design) Konferenzschrift inference procedure Spatial contextual awareness Contextual classification Land use lcsh:T business.industry Spatial database lcsh:TA1501-1820 Pattern recognition land use classification lcsh:TA1-2040 Data mining Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business computer |
| Zdroj: | Proceeding of ISPRS Geospatial Week 2015 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 2 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol II-3-W5, Pp 369-376 (2015) |
| ISSN: | 2194-9050 |
| DOI: | 10.5194/isprsannals-ii-3-w5-369-2015 |
| Popis: | Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result. |
| Databáze: | OpenAIRE |
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