A stepwise refinement classification method of remote sensing image based on feedback strategies

Xianchuan Yu, Shanshan Bian, Xin Zhou, Guian Wang, Hui He, Weijie An

Abstract: This paper focus on two phenomena that "same spectrum with different objects" and "same object with different spectra" in multispectral remote sensing image, and propose a stepwise refinement classification method based on multi-sensitive strategies. It's a top-down, gradually refinement hierarchical way of classification which combines with advantages of both supervised classification and unsupervised classification: by analyzing the characteristic of spectrum curve, cluster and find out the band combinations with big characteristic differences as the guidance of classification; according to spectral characteristics of different bands combinations, choose different methods for further fine classification. Experimental results show that the proposed method achieved the high accurate classification of multispectral remote sensing images and effectively overcome the both above phenomena. Furthermore, the overall accuracy and kappa coefficient also confirm its superior performance.