Abstract
In recent years, the compact polarimetric (CP) synthetic aperture radar (SAR) has become a hotspot of SAR Earth observation. Meanwhile, CP SAR provides both relatively rich polarization information and large swath-width for rice mapping. Fine classification of rice paddy plays an important role in growth monitoring, pest prevention and yield estimation of rice. In this study, the multi-temporal CP SAR data were firstly simulated by fully polarimetric RADARSAT-2 data, and 22 CP parameters from each of the six temporal CP SAR data were extracted. Then we built a rice height-sensitive index (RHSI). Furthermore, a decision tree (DT) method was established by using the optimal CP parameters based on RHSI. Finally, the classification results of rice paddy based on DT and support vector machine (SVM) methods were compared. Results showed that the RHSI-DT method could obtain better results, with an overall accuracy of 97.94% and a kappa coefficient of 0.973, which was 2% higher and 0.03 larger than those of the SVM method. Besides, we found that the surface scattering of m-χ decomposition (m-χ_s (0627)) and ΔShannon entropy intensity Hi (Hi (1015)-Hi (0627)) were highly effective parameters to distinguish paddies of transplanting hybrid rice (T-H) and direct-sown japonica rice (D-J).
Highlights
We select the compact polarimetric (CP) parameters that are sensitive to transplanting hybrid rice (T-H) but insensitive to direct-sown japonica rice (D-J) and the CP parameters are sensitive to D-J and insensitive to TH
In order to prove the validity of rice height-sensitive index (RHSI) and the reliability of decision tree (DT) classification method, the support vector machine (SVM) method was introduced for experimental comparison
A fine classification method of rice paddies based on DT and SVM
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Taking agricultural applications as an example, most of the studies focus on simple crop mapping, and there is insufficient research on distinguishing the different crop varieties and sowing methods and a lack of information extraction algorithms with high robustness. Guo et al [33] obtained high-precision classification results of rice paddy based on a feature selection method, SVM and DT method by using multi-temporal CP-SAR data. Brisco and Deepika’s studies showed that CPSAR data had great application potential in rice mapping Their studies only classified rice and non-rice classes, and lacked research on the different rice species and sowing methods. The research on fine classification of rice paddy based on CP SAR data is still insufficient, and there is still a lack of CP-SAR rice mapping algorithms with high robustness. A method of fine classification of rice paddy based on RHSI-DT was established to achieve highprecision mapping results of the rice paddy
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