Coastal wetland classification based on high resolution SAR and optical image fusion

Abstract

In this paper, the data source are GF-1 WFV image and Radarsat-2 SAR image covering the Yellow River Estuary wetland eastern area. The paper first uses Gram-Schmidt algorithm for fusing GF-1 image and different polarimetric mode SAR images, and then uses the method of SVM for supervised classification. Finally, the accuracy of the classification results and the capacity of information extraction are compared. The experiment results show:(1) the classification accuracy of fusing the VV polarimetric mode of SAR image and GF-1 image is better than other fusion image, reaching 83.78%, closing to the classification accuracy of GF-1 image. The classification accuracy of tidal flat reed in VV polarimetric fusion image is better than that of GF-1.(2) Tidal flat, river and aquaculture pond have the highest classification accuracy in all the fusion images.