Analysis Machine Learning Approach and Model on Hyper Spectral (Sentinel-2) Images for Land Cover Classification: Using SVM

The goal of this research study will be to research different machine learning algorithm via context oriented methodology with or without entropy for sub-pixel categorization utilizing Sentinel 2, multi-Spectral data extract reasonably accurate information for different land cover classes. To study the capabilities of Machine Learning Applications for Crop Identification and Use of temporal data information for crop planning in Machine Learning Algorithm this exploration will supportive to check Capability of Red Edge band to consolidate Crop phenology in crop recognizable proof. In this analysis work knowledge classification approach are going to be applied whereas getting ready land use and land covered map victimization for multi-spectral remote sensing knowledge sets (Sentinel-2/ Land sat). The data sets to be used in this research work will be fine spatial resolution data, to ensure classify approaches towards spatial data set and classification.