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Hyperspectral Remote Sensing Image Classification Based on Rotation Forest.

, , , and . IEEE Geosci. Remote. Sens. Lett., 11 (1): 239-243 (2014)

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Spectral-Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields., , , and . IEEE Trans. Geoscience and Remote Sensing, 53 (5): 2532-2546 (2015)Random feature subset selection in a nonstationary environment: Application to textured image segmentation., , and . ICIP, page 3028-3031. IEEE, (2008)A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors., , , , and . IEEE Trans. Image Processing, 23 (9): 4160-4174 (2014)Dynamic Feature Subspaces Selection for Decision in a Nonstationary Environment., , and . IJPRAI, 29 (6): 1551009:1-1551009:24 (2015)Dynamic Decision Method Based on Contextual Selection of Representation Subspaces., , , and . ICMLA, page 567-572. IEEE Computer Society, (2010)Feature subspaces selection via one-class SVM: Application to textured image segmentation., , and . IPTA, page 21-25. IEEE, (2010)(Semi-) Supervised Probabilistic Principal Component Analysis for Hyperspectral Remote Sensing Image Classification., , , and . IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 7 (6): 2224-2236 (2014)Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples., , , and . IEEE Trans. Geoscience and Remote Sensing, 54 (3): 1519-1531 (2016)Pansharpening using total variation regularization., , , and . IGARSS, page 166-169. IEEE, (2012)Random Subspace Ensembles for Hyperspectral Image Classification With Extended Morphological Attribute Profiles., , , , and . IEEE Trans. Geoscience and Remote Sensing, 53 (9): 4768-4786 (2015)