Multi-level threshold segmentation of high-resolution panchromatic remote sensing imagery

被引：0
|
作者：
Yang Y. [1 ]
Li Y. [1 ]
Zhao Q.-H. [1 ]
机构：
[1] Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2020年 / 28卷 / 10期
关键词：
High resolution; Image segmentation; Interval type-2 fuzzy entropy; Multi-level threshold; Optimization; Panchromatic remote sensing imagery;
D O I：
10.37188/OPE.20202810.2370
中图分类号：
学科分类号：
摘要：
To address the problems of fuzzy entropy-based multilevel threshold segmentation methods, such as insufficient fuzzy characteristics, high computational complexity, and poor automaticity, a multilevel threshold segmentation method for high-resolution panchromatic remote sensing imagery is proposed based on interval type-2 fuzzy entropy. First, a ridge-type fuzzy membership function is applied to construct an interval type-2 fuzzy set, and interval type-2 fuzzy entropy is defined in the multilevel image segmentation scene based on the constructed fuzzy set and the number of thresholds. Then, qubits encode a fuzzy parameter set as quantum chromosomes, and several quantum chromosomes are set to form the initial population. In addition, the defined interval type-2 fuzzy entropy is adopted as the fitness evaluation function to evaluate the fitness of individuals in the population, retaining and recording the best individuals. In the proposed evolutionary strategy, the dynamic rotation angle mechanism of quantum rotation gates is applied such that the population can automatically determine the optimal combination of fuzzy parameters with better adaptability and efficiency. Based on this, the multilevel threshold is obtained by the principle of maximum fuzziness, and the optimal multilevel threshold segmentation of the image is realized. In an experiment, a multilevel threshold segmentation method based on maximum entropy and fuzzy entropy was employed as the comparison algorithm to segment high-resolution panchromatic remote sensing images with different ground objects. The averages of the experimental evaluation results show that the proposed method can obtain better segmentation results while reducing the computation time. The area weighted variance is reduced by 39.7%, the Jeffries-Matusita distance is reduced by 14.7%, and the running time is 6.403 s. The method can meet the requirements of high-resolution panchromatic remote sensing image segmentation for spatial continuity and spectral uniformity, resulting in high real-time performance. © 2020, Science Press. All right reserved.
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页码：2370 / 2383
页数：13
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