计算机应用 ›› 2019, Vol. 39 ›› Issue (11): 3227-3232.DOI: 10.11772/j.issn.1001-9081.2019051043

• 2019年中国计算机学会人工智能会议(CCFAI2019)论文 • 上一篇    下一篇

基于半监督模糊C均值算法的遥感影像分类

冯国政, 徐金东, 范宝德, 赵甜雨, 朱萌, 孙潇   

  1. 烟台大学 计算机与控制工程学院, 山东 烟台 264005
  • 收稿日期:2019-05-24 修回日期:2019-06-26 发布日期:2019-09-11 出版日期:2019-11-10
  • 通讯作者: 徐金东
  • 作者简介:冯国政(1996-),男,山东潍坊人,硕士研究生,CCF会员,主要研究方向:遥感图像分类、机器学习;徐金东(1980-),男,山东招远人,副教授,博士,CCF会员,主要研究方向:图像处理;范宝德(1962-),男,山东招远人,教授,博士,主要研究方向:地学大数据分析;赵甜雨(1996-),女,山东滨州人,硕士研究生,CCF会员,主要研究方向:计算机视觉、遥感图像分类;朱萌(1995-),女,山东曲阜人,硕士研究生,CCF会员,主要研究方向:遥感图像融合;孙潇(1994-),女,山东菏泽人,硕士研究生,CCF会员,主要研究方向:高光谱图像处理、深度学习。
  • 基金资助:
    山东省自然科学基金资助项目(ZR2019MF060,ZR2017MF008,ZR201702220179,ZR201709210160);山东省高校科研计划重点项目(J18KZ016);烟台市重点研发计划项目(2018YT06000271)。

Remote sensing image classification via semi-supervised fuzzy C-means algorithm

FENG Guozheng, XU Jindong, FAN Baode, ZHAO Tianyu, ZHU Meng, SUN Xiao   

  1. School of Computer and Control Engineering, Yantai University, Yantai Shandong 264005, China
  • Received:2019-05-24 Revised:2019-06-26 Online:2019-09-11 Published:2019-11-10
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Shandong Province (ZR2019MF060, ZR2017MF008, ZR201702220179, ZR201709210160), the Key Project of Shandong Province Higher Educational Science and Technology Program (J18KZ016), the Yantai Key Research and Developement Plan (2018YT06000271).

摘要: 遥感影像数据因其固有的不确定性与复杂性,导致传统的无监督分类算法难以对其准确建模。基于模糊集理论的模式识别方法可以有效地表达数据的模糊性,其中二型模糊集能更好地刻画类间多重不确定性,而半监督法可以利用少量先验知识来解决算法对数据的泛化性问题,因此提出一种基于半监督的自适应区间二型模糊C均值遥感影像分类方法(SS-AIT2FCM)。首先,结合半监督和进化论思想,提出一种新的模糊权重指数选取方法,以提升自适应区间二型模糊C均值聚类算法的鲁棒性与泛化性,使算法更适用于光谱混叠严重、覆盖面积大、地物丰富的遥感数据分类;然后,通过对少量标记样本的软约束监督,对区间二型模糊算法迭代过程进行优化指导,来挖掘数据的最优表达。实验选用了北京颐和园区域的SPOT5多光谱遥感影像数据和广东横琴岛区域的Landsat TM多光谱遥感影像数据,对现有流行的模糊分类算法和SS-AIT2FCM的分类结果进行了比较。结果表明,SS-AIT2FCM获得了更高的分类精度与更清晰的类别边界,且有较好数据泛化能力。

关键词: 半监督, 二型模糊集, 模糊C均值算法, 遥感影像分类, 自适应区间

Abstract: Because of the uncertainty and complexity of remote sensing image data, it is difficult for traditional unsupervised algorithms to create an accurate classification model for them. Pattern recognition methods based on fuzzy set theory can express the fuzziness of data effectively. In these methods, type-2 fuzzy set can better describe inter-class hybrid uncertainty. Furthermore, semi-supervised method can use prior knowledge to deal with the generalization problem of algorithm to data. Therefore, a remote sensing image classification method based on Semi-Supervised Adaptive Interval Type-2 Fuzzy C-Means (SS-AIT2FCM) was proposed. Firstly, by integrating the semi-supervised and evolution theory, a novel fuzzy weight index selection method was proposed to improve the robustness and generalization of the adaptive interval type-2 fuzzy C-means clustering algorithm. The proposed algorithm was more suitable for the classification of remote sensing data with severe spectral aliasing, large coverage areas and abundant features. In addition, by performing soft constrained supervision on small number of labeled samples, the iterative process of the algorithm was optimized and guided, and the greatest expression of the data was obtained. In the experiments, SPOT5 multi-spectral remote sensing image data of the Summer Palace in Beijing and Landsat TM multi-spectral remote sensing image data of the Hengqin Island in Guangdong were used to compare the results of the existing fuzzy classification algorithms and SS-AIT2FCM. The experimental results show that the proposed method obtains more accurate classification and clearer boundaries of classes, and has good data generalization ability.

Key words: semi-supervised, type-2 fuzzy set, Fuzzy C-Means (FCM) algorithm, remote sensing image classification, adaptive interval

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