P
Paul D. Gader
Researcher at University of Florida
Publications - 383
Citations - 14169
Paul D. Gader is an academic researcher from University of Florida. The author has contributed to research in topics: Fuzzy logic & Hyperspectral imaging. The author has an hindex of 48, co-authored 378 publications receiving 13045 citations. Previous affiliations of Paul D. Gader include University of Wisconsin-Madison & University of Louisville.
Papers
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: An overview of unmixing methods from the time of Keshava and Mustard's tutorial as mentioned in this paper to the present can be found in Section 2.2.1].
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Twenty Years of Mixture of Experts
TL;DR: A comprehensive survey of the mixture of experts (ME), discussing the fundamental models for regression and classification and also their training with the expectation-maximization algorithm, and covering the variational learning of ME in detail.
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A Review of Nonlinear Hyperspectral Unmixing Methods
TL;DR: This paper aims to give an historical overview of the majority of nonlinear mixing models and nonlinear unmixing methods, and to explain some of the more popular techniques in detail.
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A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing
Wing-Kin Ma,Jose M. Bioucas-Dias,Tsung-Han Chan,Nicolas Gillis,Paul D. Gader,Antonio Plaza,ArulMurugan Ambikapathi,Chong-Yung Chi +7 more
TL;DR: The present development of blind HU seems to be converging to a point where the lines between remote sensing-originated ideas and advanced SP and optimization concepts are no longer clear, and insights from both sides would be used to establish better methods.