刘旋,王国胤,罗小波.基于高斯云变换的遥感图像多粒度聚类[J].计算机科学,2017,44(9):23-27, 52
基于高斯云变换的遥感图像多粒度聚类
Multi-granularity Clustering of Remote Sensing Image Based on Gaussian Cloud Transformation
投稿时间:2016-07-04  修订日期:2016-09-23
DOI:10.11896/j.issn.1002-137X.2017.09.004
中文关键词:  遥感图像,高斯云变换,多粒度,图像聚类
英文关键词:Remote sensing image,Gaussian cloud transformation,Multi-granularity,Image clustering
基金项目:本文受国家自然科学基金(61272060)资助
作者单位
刘旋 重庆邮电大学计算智能重庆市重点实验室 重庆400065 
王国胤 重庆邮电大学计算智能重庆市重点实验室 重庆400065 
罗小波 重庆邮电大学计算智能重庆市重点实验室 重庆400065 
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中文摘要:
      遥感图像技术的迅猛发展,使得传统聚类方法的局限性日益凸显。针对其信息量大、结构复杂等特点,从多粒度、多层次的角度来分析与理解地学现象,能够更好地解决遥感图像的自适应聚类问题。基于云模型与混合高斯相结合的高斯云变换是一种求解多粒度问题的新方法,能够解决问题域中多粒度的生成问题,但是其时间复杂度较高以及对噪声敏感等缺点,导致对遥感图像的聚类结果不理想。因此提出一种改进的高斯云变换方法,首先通过K-Means聚类优化初始粒度的选择,其次结合幅度云综合对粒度跃升策略进行改进,然后使用一种隶属度距离进行粒度的区域划分,最终对遥感图像进行聚类。实验结果验证了所提方法的正确性和有效性。
英文摘要:
      With the development of remote sensing image technology,the limitation of the traditional image analysis methods have become increasingly prominent.From the perspective of multi-granularity and multi-level,we can solve the adaptive clustering problem of remote sensing images better,with large amount of information and complex structures.Gaussian cloud transformation which is based on cloud model and Gaussian mixture model is a new model of multi-granularity method.It can extract multiple concepts from different granularities in a problem domain.However,due to its time complexity and noise sensitivity,the clustering result of remote sensing images is not ideal.An improved Gaussian cloud transformation method was proposed in this thesis.First,K-Means is used to optimize the selection of initial grain size and amplitude cloud comprehensive is used to modify the adaptive concept abstraction strategy.Then,the granularity division is gotten by using a membership distance.Finally,the method is applied to remote sensing images.The experimental results show the correctness and effectiveness of the proposed method.
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