HYPERSPECTRAL IMAGES AND LIDAR BASED DEM FUSION: A MULTI-MODAL LANDUSE CLASSIFICATION STRATEGY

Date

2014-07-18

Author

Demirkesen, Can
Teke, Mustafa
Sakarya, Ufuk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Hyperspectral imaging based land cover / land use classification accuracy is expected to be improved by fusion with a LIDAR based Digital Elevation Model (DEM). To this end, we propose a multi-modal architecture, as well as a filtering technique extracting a shadow invariant one dimensional feature from a pixel spectrum. The proposed approach allows treating shadow and non-shadow areas separately. DEM is incorporated into this architecture through feature extraction and post classification procedures. A digital terrain model estimated from DEM is used to calculate object heights. Slope, curvature and polynomial surface fitting based features are extracted in different scales. In post classification, DEM segments and relatively high objects obtained from DEM are interpreted by superposition with the class map.

Subject Keywords

Fusion of hyperspectral image and DEM, Shadow invariant feature, Lidar

URI

https://hdl.handle.net/11511/66796

DOI

https://doi.org/10.1109/igarss.2014.6947093

Conference Name

IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

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Citation Formats

C. Demirkesen, M. Teke, and U. Sakarya, “HYPERSPECTRAL IMAGES AND LIDAR BASED DEM FUSION: A MULTI-MODAL LANDUSE CLASSIFICATION STRATEGY,” Quebec City, CANADA, 2014, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66796.