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• W4220784838 abstract "Artificial intelligence is a robust state-of-art method for image-based mapping and classification. We presented a machine learning–based working architecture that trains its model and test to classify land cover focused toward crop field using open Sentinel-2A image data. The machine learning–based methods generally follow traditional supervised training methods and create a decision tree, weighted relation with independent inputs, and dependent outcomes. The Random forest (RF) machine learning using training samples for various land cover classes to access the statistical information to build its decision tree to derive the classes. It creates a complex decision tree of larger decision depth in comparison to other basic decision trees rule-based classification. So it manages to create decision trees between a large number of classes; however, as the number of classes is increased, time is taken to train the RF model also increases substantially. The time-series Sentinel-2A of post-monsoon Rabi crop season of the year 2019 and 2020 pre-possessed to create Soil adjusted Vegetation Index (SAVI) product followed by principal component analysis (PCA) to create the independent, nonredundant image. The independent data used for training the RF model and further classification testing. The RF machine learning model is trained using classes vectors on principal components (PCs) and for classification testing to test on PCs image to get the land cover and crop-mapped classified image. The accuracy of the classified image is tested using stratified random sampling method, and the overall accuracy is above 85% and kappa statistics is 0.78. The RF is proven for the classification of the number of classes using its complex decision-making methods with higher accuracy." @default.
• W4220784838 created "2022-04-03" @default.
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• W4220784838 date "2022-03-04" @default.
• W4220784838 modified "2023-09-25" @default.
• W4220784838 title "Artificial Machine Learning–Based Classification of Land Cover and Crop Types Using Sentinel‐2A Imagery" @default.
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• W4220784838 doi "https://doi.org/10.1002/9781119808565.ch16" @default.
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