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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.
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Journal ArticleDOI
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data
TL;DR: Experimental results clearly demonstrate that the generation of an SVM-based classifier system with RFS significantly improves overall classification accuracy as well as producer's and user's accuracies.
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Classifier ensembles for land cover mapping using multitemporal SAR imagery
Björn Waske,Matthias Braun +1 more
TL;DR: Results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number, and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.
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Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion
Björn Waske,S. van der Linden +1 more
TL;DR: It is shown that the classification of multilevel-multisource data sets with SVM and RF is feasible and does not require a definition of ideal aggregation levels.
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Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images
TL;DR: This paper presents a new method for extracting roads in Very High Resolution remotely sensed images based on advanced directional morphological operators that outperform standard approaches using rotating rectangular structuring elements.
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Mapping of hyperspectral AVIRIS data using machine-learning algorithms
TL;DR: In this paper, the performance of two machine learning techniques, namely support vector machines (SVMs) and random forests (RFs), is investigated and the classification results are compared with those from well-known methods (i.e., maximum likelihood classifier and spectral angle mapper).