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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
- J. Bioucas-Dias, A. Plaza, J. Chanussot
- Environmental Science, MathematicsIEEE Journal of Selected Topics in Applied Earth…
- 28 February 2012
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.
A Critical Comparison Among Pansharpening Algorithms
- G. Vivone, L. Alparone, L. Wald
- Environmental Science, Computer ScienceIEEE Transactions on Geoscience and Remote…
- 1 May 2015
The authors attempt to fill the gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods by offering a detailed comparison of their performances with respect to the different instruments.
A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization
- M. Simões, J. Bioucas-Dias, L. Almeida, J. Chanussot
- MathematicsIEEE Transactions on Geoscience and Remote…
- 14 November 2014
The split augmented Lagrangian shrinkage algorithm (SALSA), which is an instance of the alternating direction method of multipliers (ADMM), is added to this optimization problem, by means of a convenient variable splitting, and an effective algorithm is obtained that outperforms the state of the art.
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest
- L. Alparone, L. Wald, J. Chanussot, Claire Thomas, P. Gamba, L. Bruce
- Environmental ScienceIEEE Transactions on Geoscience and Remote…
- 24 September 2007
Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation, and they basically rely on MRA and employ adaptive models for the injection of high-pass details.
Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles
- M. Fauvel, J. Benediktsson, J. Chanussot, J. Sveinsson
- Environmental ScienceIEEE International Geoscience and Remote Sensing…
- 21 November 2008
An approach has been proposed which is based on using several principal components from the hyperspectral data and build morphological profiles which can be used all together in one extended morphological profile for classification of urban structures.
Hyperspectral Pansharpening: A Review
- Laetitia Loncan, S. Fabre, N. Yokoya
- Mathematics, Environmental ScienceIEEE Geoscience and Remote Sensing Magazine
- 17 April 2015
This work compares new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral panshARPening, which have been adapted for hypersittral data.
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
- Y. Tarabalka, M. Fauvel, J. Chanussot, J. Benediktsson
- Environmental Science, MathematicsIEEE Geoscience and Remote Sensing Letters
- 18 May 2010
A novel method for accurate spectral-spatial classification of hyperspectral images by means of a Markov random field regularization is presented, which improves classification accuracies when compared to other classification approaches.
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature
- N. Yokoya, Claas Grohnfeldt, J. Chanussot
- Environmental ScienceIEEE Geoscience and Remote Sensing Magazine
- 12 June 2017
Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually and the generalizability and versatility of the fusion algorithms are evaluated.
Advances in Spectral-Spatial Classification of Hyperspectral Images
- M. Fauvel, Y. Tarabalka, J. Benediktsson, J. Chanussot, J. Tilton
- Environmental Science, MathematicsProceedings of the IEEE
- 1 March 2013
Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information.…
Hyperspectral Remote Sensing Data Analysis and Future Challenges
- J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, J. Chanussot
- Environmental Science, MathematicsIEEE Geoscience and Remote Sensing Magazine
- 1 June 2013
A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.