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Open AccessArticle

Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function

by Chunyan Wang 1, Aigong Xu 2,* and Xiaoli Li 2
1
School of Mining Industry and Technology, Liaoning Technical University, Huludao 125105, China
2
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 710; https://doi.org/10.3390/rs10050710
Received: 14 March 2018 / Revised: 18 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
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. View Full-Text
Keywords: interval type-2 fuzzy membership function; weighted average; high resolution; remote sensing image classification; type-1 fuzzy membership function interval type-2 fuzzy membership function; weighted average; high resolution; remote sensing image classification; type-1 fuzzy membership function
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MDPI and ACS Style

Wang, C.; Xu, A.; Li, X. Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function. Remote Sens. 2018, 10, 710.

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