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.
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Acknowledgements
The authors would like to thank the Indian Space Research Organization (ISRO) for supporting this research work via the ISRO RESPOND project ISRO/RES/4/685/20-21.
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Ramathilagam, A.B., Natarajan, S., Kumar, A. (2022). Crop Classification in the Cauvery Delta Zone Using Machine Learning on Multi-Sensor Data. In: Kumar, R., Ahn, C.W., Sharma, T.K., Verma, O.P., Agarwal, A. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-19-0707-4_20
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DOI: https://doi.org/10.1007/978-981-19-0707-4_20
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