Image segmentation is a significant step in image processing. High-resolution remote sensing images can clearly characterize landscape information and eliminate the membership uncertainty caused by mixed pixels. It has considerable advantages and potential in precise segmentation. Nonetheless

the spatial complexity caused by spectral measurement and the enhanced differences among pixels in the same object may reduce segmentation result accuracy. Thus

this study establishes an interval type-2fuzzy model for detected images. Interval type-2 fuzzy theory considers the main and the secondary membership functions; the latter is represented by the label"membership 1"to express the uncertainty of pixel membership and of a segmentation decision. The aforementioned problem is solved effectively with the proposed method. The drop type problem in type-2 fuzzy theory is anotherfocus of research in this field; a fuzzy decision model is established by reducing the type-2 fuzzy model to a type-1 model. The fuzzy decision model directly influences segmentation accuracy

and recent studies are all based on the upper and lower membership functions. These models can improve decision quality to some extent; however

they do not consider the main membership function. This neglect may significantly influence decision results

particularly when the influence of neighborhood pixels cannot be incorporated into the supervised image segmentation algorithm. To overcome these shortcomings

we proposed high-resolution remote sensing image segmentation by introducing a spatial relationship into the interval type-2 fuzzy model. The proposed algorithm considers the influences of the upper

lower

and the main membership functions in establishing the fuzzy decision model.First

a type-1 fuzzy model is built with the Gauss function to characterize the uncertainty of pixel membership. Then

we extend the mean and variance of this model to construct the type-2 fuzzy model

which improves the expression of the membership function in the type-1 fuzzy model and serves as the knowledge basis to enhance segmentation decision accuracy. A segmentation decision model is then established based on the information derived from the upper

lower

and main membership functions of the trained data. Finally

the membership of a pixel is decided by the membership functions of both the pixel itself and its neighbors to optimize the segmentation of high-resolution remote sensing images.We compare this method with maximum likelihood segmentation and an interval type-2 fuzzy model segmentation without a spatial relationship via high-resolution real images. Qualitative and quantitative analysis findings indicate that the method applied in this study generates high segmentation accuracy.This study proposes a supervised image segmentation method based on an interval type-2 fuzzy model with a spatial relationship. This method improves the uncertainty expression of pixel membership

solves the problems caused by complicated spatial relevance

and enhances the accuracy of the segmentation strategy. Furthermore

the experiments show that this method is effective and feasible. In the future

the Gauss mixture model will be used as a type-1 fuzzy model to potentially improve the accurate characterization of landscape features.