[1]李奇生,赵成萍,尹子琴,等.均衡FCM算法在农作物遥感影像解译中的应用[J].江苏农业学报,2020,(05):1163-1168.[doi:doi:10.3969/j.issn.1000-4440.2020.05.013]
　LI Qi-sheng,ZHAO Cheng-ping,YIN Zi-qin,et al.Application of balanced FCM algorithm on the interpretation of crops remote sensing image[J].,2020,(05):1163-1168.[doi:doi:10.3969/j.issn.1000-4440.2020.05.013]
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均衡FCM算法在农作物遥感影像解译中的应用()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年05期
页码:
1163-1168
栏目:
耕作栽培·资源环境
出版日期:
2020-10-31

文章信息/Info

Title:
Application of balanced FCM algorithm on the interpretation of crops remote sensing image
作者:
李奇生1; 赵成萍1; 尹子琴2; 李博3; 周新志1
（1.四川大学电子信息学院，四川成都610065；2.云南省云计算学会，云南昆明650032；3.四川大学水利水电学院，四川成都610065）
Author(s):
LI Qi-sheng1; ZHAO Cheng-ping1; YIN Zi-qin2; LI Bo3; ZHOU Xin-zhi1
(1.College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China;2.Cloud-computing Academic Society of Yunnan Province, Kunming 650032, China;3.College of Water Resource & Hydropower, Sichuan University, Chengdu 610065, China)
关键词:
均衡C-均值聚类算法（均衡FCM算法）; 混合像元; 面积提取; 图像分类
Keywords:
fuzzy C-means clustering algorithm based on balanced data sets(BDS-FCM algorithm); mixed pixel; area extraction; image classification
分类号:
S127
DOI:
doi:10.3969/j.issn.1000-4440.2020.05.013
文献标志码:
A
摘要:
针对传统的模糊C-均值聚类算法（FCM算法）对大数据集收敛速度慢，聚类不均匀类别样本时出现大类吃小类现象以及对初始聚类中心点要求高等问题，提出了一种基于均衡样本集思想的模糊C-均值聚类算法（均衡FCM算法）。选取Landsat8、Sentinel2A遥感卫星采集获得的哈尔滨市宾县2018年遥感图像，验证方法的有效性。结果显示，提出的均衡FCM算法可以改善传统FCM算法存在的问题，验证了均衡FCM算法的有效性。
Abstract:
To solve the conventional fuzzy C-means clustering algorithm(FCM algorithm) problems including slow convergence speed for large data sets, the occurrence of neglect of smaller clustered groups when the clustering categories are uneven, and high requirement on the initial clustering center points, this paper proposed a fuzzy clustering algorithm model based on balanced data sets (BDS-FCM algorithm). To verify the effectiveness, the remote sensing images of Bin County, Harbin City collected by Landsat8 and Sentinel2A remote sensing satellites in 2018 was selected as experimental subjects. Results of the experiment show that the proposed BDS-FCM algorithm can improve the conventional FCM algorithm and verify the effectiveness of BDS-FCM.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2019-11-08基金项目：国家自然科学基金项目（U1933123）作者简介：李奇生(1996-)，男，河南洛阳人，硕士研究生，研究方向为模式识别与智能系统。(E-mail)563692411@qq.com通讯作者：赵成萍，(E-mail)sc_zcp@scu.edu.cn
更新日期/Last Update: 2020-11-16