An interval Type-2 fuzzy sets generation method for remote sensing imagery classification

In this paper, we focus on the uncertainty in land cover classification that is caused by the heterogeneity of similar ground objects and the complex spatial correlation of remote sensing image pixels. We propose a method for generating interval type-2 fuzzy sets that incorporates neighborhood information to improve the classification accuracy. First, a correlation model between neighboring pixels is established, and it is used to filter remote sensing images. Then, the interval type-2 fuzzy sets are generated by using the original image pixels and the filtered pixels. Finally, an interval type-2 fuzzy clustering method based on neighborhood information is proposed. To demonstrate the advantages and effectiveness of the proposed method, we ran experiments utilizing two remote sensing images with complex land cover. The results indicate that the proposed method considers both the color information in a sample and its neighborhood relationship. The proposed method can effectively suppress the heterogeneity of the same objects in remote sensing images and can improve the classification accuracy.