Abstract
Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm.
Highlights
Image classification is the basic task for the processing of remote-sensing images
Contains four ground object types: forest (ClaRssemIo)t,emSeinnse. 2(0C18la, 1s0s, IxIF),OfRarPmEElaRnRdEV(CIElWass III), and residential area (Class IV), where pits and rocks exist in the mining area (Class II), two kinds of crops with different grayscales exist in the farmland area (Class III), and houses, roads, and vegetation exist in the residential area (Class IV)
Because there exist the obvious difference in grayscale measure among the three kinds of regions to be classified and the salt-and-pepper noise can be handled effectively by the HMRF-Fuzzy C-means (FCM) method, the classification accuracy of the HMRF-FCM method is higher than that weighted average method is used, the total classification accuracy of the training data is 1.000 and the Kappa coefficient is 0.999
Summary
Image classification is the basic task for the processing of remote-sensing images. Its results will greatly affect the accuracy of the subsequent tasks, such as feature extraction, target recognition, and ground object classification. To address the uncertainty of pixel class and the classification decision of the high-resolution remote-sensing data, this paper proposes a weighted average supervised classification method based on interval type-2 fuzzy theory.
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