Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

Rice is an important food crop for human beings.Accurately distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern agriculture.Compact polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and mapping.Currently,the use of machine learning methods to build classification models is a controversial topic.In this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice paddies.First,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data sets.Second,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data sets.In addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice paddies.The results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than 0.937.Among them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,respectively.For the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

Oil spill detection on the ocean surface using hybrid polarimetric SAR imagery

Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future. Here, we show how the second Stokes parameter of hybrid-polarimetric SAR can be employed to detect oil on the ocean surface using the classic well-known Otsu threshold methodology, in relation to contributions from different polarizations and dampening effects on backscatter intensity, neglecting the specific scattering mechanisms and oil types for an oil-covered surface. The detection methodology is demonstrated to be reliable in three example cases: oil-on-water experiments conducted by the Norwegian Clean Seas Association, natural oil seeps from the Gulf of Mexico, and observations from the Deep Water Horizon oil spill disaster in 2010.