像素
数学
模式识别(心理学)
模糊逻辑
离散化
模糊集
粗集
特征(语言学)
算法
人工智能
计算机科学
数学分析
语言学
哲学
作者
Qiong Chen,Weiping Ding,Xiaomeng Huang,Hao Wang
出处
期刊:IEEE Transactions on Fuzzy Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:31 (3): 845-859
被引量:16
标识
DOI:10.1109/tfuzz.2022.3190625
摘要
Feature discretization algorithms of remote sensing images are often based on the assumption that a sample only belongs to a single category and cannot describe uncertainty caused by mixed pixels. Fuzzy rough models quantify uncertain information by introducing the memberships of pixels to each category. However, there are large errors in the decomposition model of mixed pixels, making the obtained memberships fuzzy. To overcome this weakness, we propose a feature discretization algorithm based on the generalized interval type-II fuzzy rough set for mixed pixels (GIT2FRSD). We use the fuzzy mean vector and the fuzzy covariance matrix to calculate the primary grades of pixels to each ground object and determine the secondary grades according to the distribution of pixels in the boundary region of the rough set. Then, we construct the fitness function using the magnitude of the reduction of the number of breakpoints and the average approximation precision of the generalized interval type-II fuzzy rough set and search for the best discrete breakpoints in all bands of the remote sensing image using an adaptive genetic algorithm. Our method further fuzzifies the abundance information, more accurately quantifying and evaluating the uncertainty caused by mixed pixels at a time complexity similar to that of the fuzzy rough model. The experimental results on GF-2 and Landsat 8 images show that compared with current mainstream discretization algorithms, our method has better search efficiency. It obtains the minimum number of discrete intervals while ensuring data consistency and achieves the highest classification accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI