Method of winter wheat planting area estimation based on support vector machine and post-classification changed vector analysis

The crop phenology characristics can greatly improve estimation of planted area while using remote sensed technologies. Taking Southeast Beijing as the study area in this paper, the support vector machine (SVM) dichotomy model and post-classification changed vector analysis (PCVA) model were integrated to estimate winter wheat area. The results indicate that as follows: The overall pixel accuracy and Kappa coefficient resulted from this proposed method were 95% and 0.90, which were much better than those from post-classification comparison method (91% and 0.79). The combining of SVM and PCVA models also presented a good help on the selection of changing threshold value which tended to be subjective. Besides, with the polarization phenomenon of the frequency histogram in this method, it decreased the partial frequency of change threshold value and led to a lower threshold sensitivity, thus the determination of threshold value was more objective. The combining use of SVM and PCVA models was more sensitive to spectral changes, and improved the detection of crop growth change under different growing stages, as well as the estimating accuracy on winter wheat planted area. It is believed that this method also has a great potential for other crops planted area estimates.