On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
The nature of statistical learning theory
The nature of statistical learning theory
The ordered weighted averaging operators: theory and applications
The ordered weighted averaging operators: theory and applications
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Generalized OWA Aggregation Operators
Fuzzy Optimization and Decision Making
On generalized Bonferroni mean operators for multi-criteria aggregation
International Journal of Approximate Reasoning
Power-geometric operators and their use in group decision making
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
A lattice matrix method for hyperspectral image unmixing
Information Sciences: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the fusion of imprecise uncertainty measures using belief structures
Information Sciences: an International Journal
Models to determine parameterized ordered weighted averaging operators using optimization criteria
Information Sciences: an International Journal
Intuitionistic Fuzzy Bonferroni Means
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Lossy-to-lossless 3D image coding through prior coefficient lookup tables
Information Sciences: an International Journal
Combining supervised and unsupervised models via unconstrained probabilistic embedding
Information Sciences: an International Journal
Information Sciences: an International Journal
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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In this paper, we introduce a novel framework for improved classification of hyperspectral images based on the combination of supervised and unsupervised learning paradigms. In particular, we propose to fuse the capabilities of the support vector machine classifier and the fuzzy C-means clustering algorithm. While the former is used to generate a spectral-based classification map, the latter is adopted to provide an ensemble of clustering maps. To reduce the computation complexity, the most representative spectral channels identified by the Markov Fisher Selector algorithm are used during the clustering process. Then, these maps are successively labeled via a pairwise relabeling procedure with respect to the pixel-based classification map using voting rules. To generate the final classification result, we propose to aggregate the obtained set of spectro-spatial maps through different fusion methods based on voting rules and Markov Random Field theory. Experimental results obtained on two hyperspectral images acquired by the reflective optics system imaging spectrometer and the airborne visible/infrared imaging spectrometer, respectively; confirm the promising capabilities of the proposed framework.