Abstract: In order to accurately analyze the spatial-temporal evolution of land in functional areas, it is necessary to accurately distinguish the relevant characteristics of land spatial-temporal change. This paper designs the feature extraction model of land spatial-temporal change in functional areas. By using full polarization decomposition and gray level co-occurrence matrix,all kinds of scattering features and texture features of different objects in SAR image which reflect the spatiotemporal change of land in functional area are classified, and the best weighted full polarization feature combination is determined.The combination is input into random forest model to complete the classification of ground objects in the final image and realize the feature extraction of spatiotemporal change of land in functional area. The test results show that: the model uses weighted full polarization feature combination,which can accurately describe the distribution of surface features and ensure the reliable classification of surface features.Taking the spatial-temporal change characteristics of land in an ecological function area of Hunan province as an example, it can achieve good extraction effect.

Key words: weighted total polarization, SAR image classification, functional area land, spatiotemporal variation, feature extraction, characteristics combination