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Evaluation of Multi-Temporal Sentinel-1 Dual Polarization SAR Data for Crop Type Classification
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Author(s): Thota Sivasankar (NIIT University, India), Pavan Kumar Sharma (Amnex Infotechnologies Pvt. Ltd., India), M. N. S. Ramya (Independent Researcher, USA), Pithani Venkatesh (Skymet Weather Services Pvt. Ltd., India)and G. D. Bairagi (M.P. Council of Science and Technology, India)
Copyright: 2020
Pages: 18
Source title:
Spatial Information Science for Natural Resource Management
Source Author(s)/Editor(s): Suraj Kumar Singh (Suresh Gyan Vihar University, Jaipur, India), Shruti Kanga (Suresh Gyan Vihar University, Jaipur, India)and Varun Narayan Mishra (Suresh Gyan Vihar University, Jaipur, India)
DOI: 10.4018/978-1-7998-5027-4.ch003
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Abstract
India is one of the highly populated countries, and its economy mainly depends on agriculture. The crop type classification is an essential requirement for ensuring food security, crop monitoring, and to understand the environmental consequences of cultivated ecosystems. This study exploits freely available multi-temporal SAR data for discriminating crop types, such as wheat, gram, and mustard, over Ashok Nagar district, Madhya Pradesh, India. Nine Sentinel-1 dual-polarized data acquired from January 2018 to April 2018 in interferometric wide swath mode are used. Class separability analysis using Bhattacharyya Distance (BD) has been performed for multi-temporal VV and VH backscatter, log-ratio, and Radar Vegetation Index (RVI) to quantify the ability to distinguish temporal profiles of crops. RVI has shown the significant result in class separability analysis in comparison with other parameters. Crop type classification map has been generated using a support vector machine classifier with overall accuracy and Kappa coefficient of 96.32% and 0.95, respectively.
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