M
Michal Shimoni
Researcher at Royal Military Academy
Publications - 75
Citations - 1298
Michal Shimoni is an academic researcher from Royal Military Academy. The author has contributed to research in topics: Hyperspectral imaging & Emissivity. The author has an hindex of 12, co-authored 69 publications receiving 928 citations. Previous affiliations of Michal Shimoni include Military Academy.
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Journal ArticleDOI
Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest
Wenzhi Liao,Xin Huang,Frieke Van Coillie,Sidharta Gautama,Aleksandra Pizurica,Wilfried Philips,Hui Liu,Tingting Zhu,Michal Shimoni,Gabriele Moser,Devis Tuia +10 more
TL;DR: The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data, resulting in the results obtained by the winners of both tracks.
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Hypersectral Imaging for Military and Security Applications: Combining Myriad Processing and Sensing Techniques
TL;DR: The military- and security-driven applications of HSI are reviewed, which analyzes and demonstrates sensing capabilities and advanced methodologies, summarizes the current spaceborne and airborne military HSI technologies, and reviews future technological developments.
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Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application
Stefanos Georganos,Taïs Grippa,Sabine Vanhuysse,Moritz Lennert,Michal Shimoni,Stamatis Kalogirou,Eléonore Wolff +6 more
TL;DR: A new metric to perform model selection named classification optimization score (COS) that rewards model simplicity and indirectly penalizes for increased computational time and processing requirements using the number of features for a given classification model as a surrogate is proposed.
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Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting
Stefanos Georganos,Taïs Grippa,Sabine Vanhuysse,Moritz Lennert,Michal Shimoni,Eléonore Wolff +5 more
TL;DR: The results demonstrate that Xgboost parameterized with a Bayesian procedure, systematically outperformed RF and SVM, mainly in larger sample sizes.
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Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A: 2-D Contest
Manuel Campos-Taberner,Adriana Romero-Soriano,Carlo Gatta,Gustau Camps-Valls,Adrien Lagrange,Bertrand Le Saux,Anne Beaupere,Alexandre Boulch,Adrien Chan-Hon-Tong,Stéphane Herbin,Hicham Randrianarivo,Marin Ferecatu,Michal Shimoni,Gabriele Moser,Devis Tuia +14 more
TL;DR: The scientific results obtained by the winners of the 2-D contest are discussed, which studied either the complementarity of RGB and LiDAR with deep neural networks or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data.