Fusion of Support Vector Machines for Classification of Multisensor Data

By Björn Waske, Jon Atli Benediktsson. IEEE Transactions on Geoscience and Remote Sensing, 2007

449 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/0584f969df7f58b9682b8b28a732800399a2d6ea

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Citations in the corpus

Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery (2009, methodology): “It has been successfully applied to multisensor information fusion [7] and object-based classification [10].”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “Joint optical-SAR data classification has been addressed for a couple of decades and many methodological approaches, including statistical pattern recognition [23], [77], neural networks [78], decision fusion [79], evidence theory [80], kernel-based learning [76], [81], and MRFs [82], have been prop”
Monitoring Land-Cover Changes: A Machine-Learning Perspective (2016, unknown): “[20] B.”
Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery (2017, methodology): “For example, Du et al. [24] proposed an algorithm that fuses binary classiﬁers together using Choquet integration applied to hyperspectral imagery, while Waske and Benediktsson [25] fused SVM trained on synthetic aperture radar data and multispectral data.”
Support vector machines in remote sensing: A review (2011, unknown): “Waske and Benediktsson (2007) classified multi-sensor (SAR, Landsat TM, and SPOT) and multi-temporal data through data fusion based on SVMs.”
An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery (2013, methodology): “Furthermore, the VS approach is often jointly used with an SVM classifier since SVM is not constrained to prior assumptions on the distribution of input data, and it enables the weighting of the different features [34].”
Machine Learning With Big Data: Challenges and Approaches (2017, methodology): “Waske and Benediktsson [123] have applied SVM for individual learners and also used an SVM in the voting process.”
Hyperspectral Image Classification With Independent Component Discriminant Analysis (2011, background): “SVMs, in particular, have been widely investigated recently in the remote sensing community [24], [25], [29], [30].”
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011, background): “Waske and Benediktsson (2007) applied it on SAR and multispectral images.”
On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (2012, methodology): “The support vector machine (SVM) classifier [47], which has been reported to be effective in the classification of hyperdimensional feature sets [48], was used to interpret the aforementioned processed feature data.”
Multi-source remote sensing data fusion: status and trends (2010, methodology): “In the recent literature, the remote sensing data fusion community has proposed high-level multi-source fusion methods based on Markov random field (MRF; Solberg et al. 1996), support vector machines (SVM) (Waske and Benediktsson 2007, Waske and van der Linden 2008), and the decision fusion approach”