An assessment of temporal RADARSAT-2 SAR data for crop classification using KPCA based support vector machine

Abstract

Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development of crops lead to dissimilar temporal signatures of backscatter intensities, which may influence the separability of the crop classes. This principle leads to multi-date classification approach. In this work, kernel principal component (KPCA) is adopted for feature selection from multi-date datasets, and the selected features are used for classification using support vector machine (SVM) classifier. The classification is investigated for both the KPCA-based SVM and only SVM approaches using quad-pol C-band RADARSAT-2 data acquired over the test site in Vijayawada, India. KPCA-based SVM classification shows an overall accuracy of 89%, which is better than 82% obtained using the SVM-based classification. The proposed methodology effectively incorporates the temporal crop information during classification.

Keywords:

• Multi-temporal
• phenology
• SAR
• crop classification

Acknowledgements

Authors would like to thank the Canadian Space Agency (CSA) and MAXAR Technologies Ltd. (formerly MDA) for providing RADARSAT-2 data for the JECAM SAR Inter-Comparison Experiment. The authors are also thankful to the Andhra Pradesh Space Application Centre (APSAC), ITE & C Department, Government of Andhra Pradesh for support during the field campaigns. Authors acknowledge the GEO-AWS Earth Observation Cloud Credits Programme, which supported the computation on AWS cloud platform through the project ‘AWS4AgriSAR-Crop inventory mapping from SAR data on cloud computing platform’.

Disclosure statement

No potential conflict of interest was reported by the author(s).