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A hierarchical naive Bayesian network classifier embedded GMM for textural image.

, , , and . Int. J. Appl. Earth Obs. Geoinformation, 14 (1): 139-148 (2012)

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Spatial-Temporal Dynamics of Cropping Frequency in Hubei Province over 2001-2015., , and . Sensors, 17 (11): 2622 (2017)Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data., , and . Remote Sensing, 11 (2): 168 (2019)From frequency to intensity - A new index for annual large-scale cropping intensity mapping., , , , , and . Comput. Electron. Agric., (December 2023)A hierarchical naive Bayesian network classifier embedded GMM for textural image., , , and . Int. J. Appl. Earth Obs. Geoinformation, 14 (1): 139-148 (2012)Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt., , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2020)Estimating Agricultural Cropping Intensity Using a New Temporal Mixture Analysis Method from Time Series MODIS., , , , and . Remote. Sens., 15 (19): 4712 (October 2023)A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland., , , , , , , and . Remote. Sens., 10 (11): 1831 (2018)A study of vegetation phenology in the analysis of urbanization process based on time-series MODIS data., , , and . IGARSS, page 2826-2829. IEEE, (2016)Exploring cropping intensity dynamics by integrating crop phenology information using Bayesian networks., , , and . Comput. Electron. Agric., (2022)