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遥感技术与应用  2019, Vol. 34 Issue (4): 799-806    DOI: 10.11873/j.issn.1004-0323.2019.4.0799
数据与图像处理     
一种结合空间与光谱信息的改进CVA变化检测方法
申祎(),王超(),胡佳乐
南京信息工程大学电子与信息工程学院,江苏 南京 210044
An Improved CVA Change Detection Method Combining Spatial and Spectral Information
Yi Shen(),Chao Wang(),Jiale Hu
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
 全文: PDF(7225 KB)   HTML
摘要:

基于变化向量分析(CVA)的变化检测方法通过直接比较像素差异,能够快速提取多时相影像间的变化信息。尽管如此,由于忽略了像素领域的空间上下文信息及波段之间的差异性和互补性,导致检测结果中难以消除噪声等因素产生的“伪变化”。为此提出了一种结合空间和光谱信息的改进CVA方法。首先,采用主成分分析法对影像进行增强,继而通过构建一种新的多方向差分描述子来提取中心像素的空间上下文信息;在此基础上,提出一种基于相关性的加权融合策略,获得统一的变化强度差分影像;最后,采用EM算法求得变化像素的阈值,继而得到二值检测结果。实验结果表明:所提出的算法能够有效应对“伪变化”的干扰,显著提高变化检测的精度及可靠性。

关键词: CVA变化检测多方向光谱加权EM算法    
Abstract:

Change detection based on change vector analysis can quickly extract change information between multi-temporal images by directly comparing pixel differences. However, because the spatial context information in the pixel field and the difference and complementarity between bands are ignored, it is difficult to eliminate the "pseudo-changes" caused by noise and other factors in the detection results. In view of this, this paper proposes a method for detecting changes in spatial and spectral information. Firstly, the image is enhanced by the principal component analysis method, and then spatial context information of pixels is extracted by constructing a new multi-directional differential descriptor; On this basis, a spectrally weighted fusion strategy based on inter-band correlation is proposed to obtain a uniform variation intensity difference image Finally, the EM algorithm is adopted to confirm the final change pixels. The experimental results show that the proposed algorithm can effectively deal with the "pseudo-change" interference and significantly improve the accuracy and reliability of the change detection.

Key words: CVA    Change detection    Multidirectional    Spectral weighting    EM algorithm
收稿日期: 2018-04-25 出版日期: 2019-10-16
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(61601229);江苏省自然科学基金项目(BK20160966);江苏省高校自然科学基金项目(16KJB510022);信国家重点实验室开放研究基金项目(2012D20);江苏省高等学校优势学科项目(1081080015001)
通讯作者: 王超     E-mail: 15951676162@163.com;chaowang@nuist.edu.cn
作者简介: 申 祎(1995-),男,河南新乡人,硕士研究生, 主要从事遥感图像变化检测方面的研究。E?mail:15951676162@163.com
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引用本文:

申祎,王超,胡佳乐. 一种结合空间与光谱信息的改进CVA变化检测方法[J]. 遥感技术与应用, 2019, 34(4): 799-806.

Yi Shen,Chao Wang,Jiale Hu. An Improved CVA Change Detection Method Combining Spatial and Spectral Information. Remote Sensing Technology and Application, 2019, 34(4): 799-806.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.4.0799        http://www.rsta.ac.cn/CN/Y2019/V34/I4/799

图1  遥感影像变化检测流程
图2  多方向差分描述子
图3  实验数据1
图4  实验数据1检测结果
方法\指标

错误像素

/个

误检像素

/个

漏检像素

/个

Kappa

/%

提出的算法2 5431 2991 2350.756 9
CVA-EM3 7122 1141 5980.623 1
仅采用多方向差分描述子2 9961 7431 2530.708 7
仅采用融合策略3 2312 0151 2160.687 1
表 1  实验数据1变化检测精度及误差
图6  实验数据2检测结果
图5  实验数据2
方法\指标错误像素/个误检像素/个漏检像素/个Kappa
提出的算法2 8331 6341 1990.712 7
CVA-EM4 2782 1562 1220.603 9
仅采用多方向差分描述子3 0661 7131 3530.688 0
仅采用融合策略3 8191 9201 8990.638 1
表 2  实验数据2变化检测精度及误差
图7  阈值对错误像素的影响
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