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Combining rotation forest and multi-scale segmentation for the classification of hyperspectral data.

, , , and . WHISPERS, page 1-4. IEEE, (2015)

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Bi-CCD: Improved Continuous Change Detection by Combining Forward and Reverse Change Detection Procedure., , , , , , and . IEEE Geosci. Remote. Sens. Lett., (2022)Direct, ECOC, ND and END Frameworks - Which One Is the Best? An Empirical Study of Sentinel-2A MSIL1C Image Classification for Arid-Land Vegetation Mapping in the Ili River Delta, Kazakhstan., , , , , , , , , and . Remote. Sens., 11 (16): 1953 (2019)On the framework, algorithms and applications of hyperspectral remote sensing data mining., and . IGARSS, page 4. IEEE, (2005)Combining Rotation Forest and Multiscale Segmentation for the Classification of Hyperspectral Data., , , and . IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 9 (9): 4060-4072 (2016)(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)Urban Thermal Environment Simulation and Prediction based on Remote Sensing and GIS., , and . IGARSS (3), page 1055-1058. IEEE, (2009)Multi-objective processing of ASTER image for urban environmental analysis., , , and . IGARSS, page 675-678. IEEE, (2007)Hyperspectral band selection using firefly algorithm., , and . WHISPERS, page 1-4. IEEE, (2014)Active extreme learning machines for quad-polarimetric SAR imagery classification., , , and . Int. J. Appl. Earth Obs. Geoinformation, (2015)Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples., , , and . IEEE Trans. Geosci. Remote. Sens., 54 (3): 1519-1531 (2016)