L
Luis Gómez-Chova
Researcher at University of Valencia
Publications - 147
Citations - 6102
Luis Gómez-Chova is an academic researcher from University of Valencia. The author has contributed to research in topics: Kernel method & Support vector machine. The author has an hindex of 34, co-authored 144 publications receiving 5257 citations. Previous affiliations of Luis Gómez-Chova include Manipal University Jaipur.
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Journal ArticleDOI
Composite kernels for hyperspectral image classification
Gustau Camps-Valls,Luis Gómez-Chova,Jordi Muñoz-Marí,Joan Vila-Francés,Javier Calpe-Maravilla +4 more
TL;DR: This framework of composite kernels demonstrates enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only, flexibility to balance between the spatial and spectral information in the classifier, and computational efficiency.
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Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
Gustau Camps-Valls,Luis Gómez-Chova,Jordi Muñoz-Marí,José Luis Rojo-Álvarez,Manel Martínez-Ramón +4 more
TL;DR: A general framework based on kernel methods for the integration of heterogeneous sources of information for multitemporal classification of remote sensing images and the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods is presented.
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Multimodal Classification of Remote Sensing Images: A Review and Future Directions
TL;DR: A taxonomical view of the field is provided and the current methodologies for multimodal classification of remote sensing images are reviewed, which highlight the most recent advances, which exploit synergies with machine learning and signal processing.
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Robust support vector method for hyperspectral data classification and knowledge discovery
Gustau Camps-Valls,Luis Gómez-Chova,Javier Calpe-Maravilla,José D. Martín-Guerrero,Emilio Soria-Olivas,L. Alonso-Chorda,Jose Moreno +6 more
TL;DR: Support vector machines yield better outcomes than neural networks regarding accuracy, simplicity, and robustness, and training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data.
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Semisupervised Image Classification With Laplacian Support Vector Machines
TL;DR: The Laplacian SVM (LapSVM) is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical.