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Björn Waske
Researcher at Free University of Berlin
Publications - 84
Citations - 4935
Björn Waske is an academic researcher from Free University of Berlin. The author has contributed to research in topics: Support vector machine & Contextual image classification. The author has an hindex of 28, co-authored 80 publications receiving 4217 citations. Previous affiliations of Björn Waske include University of Osnabrück & University of Iceland.
Papers
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Morphological Attribute Profiles for the Analysis of Very High Resolution Images
TL;DR: The classification maps obtained by considering different APs result in a better description of the scene than those obtained with an MP, and the usefulness of APs in modeling the spatial information present in the images is proved.
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A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring
Neha Joshi,Matthias Baumann,Andrea Ehammer,Rasmus Fensholt,Kenneth Grogan,Patrick Hostert,Martin Rudbeck Jepsen,Tobias Kuemmerle,Patrick Meyfroidt,Edward T. A. Mitchard,Johannes Reiche,Casey M. Ryan,Björn Waske +12 more
TL;DR: This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments, and concluded that fusion improved results compared to using single data sources.
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Fusion of Support Vector Machines for Classification of Multisensor Data
TL;DR: The proposed SVM-based fusion approach outperforms all other approaches and significantly improves the results of a single SVM, which is trained on the whole multisensor data set.
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Extended profiles with morphological attribute filters for the analysis of hyperspectral data
TL;DR: These extended profiles are based on morphological attribute filters and are capable of extracting spatial features that can better model the spatial information, with respect to conventional extended morphological profiles.
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Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features
TL;DR: Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies.