Abstract
Synthetic aperture radar (SAR) images (microwave images) and optical ones have been recognized as important sources to study land use and land cover. The aim of this study is to create land use/cover classification using the maximum likelihood (ML) and support vector machines (SVM) algorithms. Essential geo corrections were applied to the images at the pre-processing stage. To evaluate both the classified images, the metrics of overall accuracy and kappa coefficient were used. The so-evaluated accuracy assessment results demonstrated that the SVM algorithm gave an accuracy of 88.94 and 77.89% in optical and SAR images, respectively, and the kappa coefficients in the same order being 0.87 and 0.75 approximately. The kappa coefficient of the SVM is higher than that of the ML algorithm, both in the case of optical and microwave classified data. Therefore, the SVM algorithm is suggested to be used as an image classifier for both optical and SAR (microwave) high-resolution images.
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Acknowledgments
The authors would like to thank all the reviewers, Dr. Rajashree Bothale, General Manager, Outreach Facility of NRSC and Director of the NRSC (ISRO), Hyderabad for their encouragement. They would also like to extend their sincere thanks to the institute staff for the technical support and remarkable suggestions during research work. The authors would like to acknowledge the CSIR fellowship provided by Govt. of India, New Delhi, India.
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Balnarsaiah, B., Prasad, T.S., Parayitam, L., Penta, B., Patibandla, C. (2020). Classifications of High-Resolution SAR and Optical Images Using Supervised Algorithms. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_84
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DOI: https://doi.org/10.1007/978-981-13-9042-5_84
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