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- W4285158050 abstract "Accurate crop classification is one of the most important applications of remote sensing. This task becomes more challenging when the agricultural fields are smallholdings and heterogeneous. This study intends to address this issue with the synergistic use of synthetic aperture radar data of Sentinel-1 and multispectral data of Sentinel-2 in the Cauvery delta zone. Single date reflectance values and spectral indices from Sentinel-2 data were used along with the multi-temporal dual-pol backscatter data from Sentinel-1 data to train random forest classifier. The multi-sensor Sentinel dataset achieved the highest accuracy score and Cohen’s kappa value of 0.990 and 0.989." @default.
- W4285158050 created "2022-07-14" @default.
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- W4285158050 date "2022-01-01" @default.
- W4285158050 modified "2023-10-16" @default.
- W4285158050 title "Crop Classification in the Cauvery Delta Zone Using Machine Learning on Multi-Sensor Data" @default.
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- W4285158050 doi "https://doi.org/10.1007/978-981-19-0707-4_20" @default.
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