摘要
根据高空间分辨率影像上变化区域呈聚集状分布的特点,提出了一种面向地理对象的遥感影像变化检测算法。在利用Mean-Shift分割算法的基础上,获得不同时相地理对象的灰度特征信息,结合变化矢量分析,采用最大数学期望算法自动提取变化区域。以QuickBird、SPOT、TM三组不同空间分辨率的影像进行算法验证并比较了该方法与单像素变化检测算法的差异。结果表明,三组影像中面向对象的变化检测算法的检测精度分别为91.1%,87.3%和84.3%,单像素的变化检测算法检测精度分别为86.41%,82.48%和81.02%。试验结果显示面向对象的算法检测精度高于基于单像素的变化检测算法,且对高空间分辨率的影像检测效果要优于对中低空间分辨率的影像的检测效果。该算法减少了变化阈值确定中的人工干预,克服了以像素为单位的变化检测算法中由于缺少空间邻域信息而产生孤立、离散、不连通变化结果的问题,能够满足在不同土地覆盖类型下的变化检测要求,在国土资源监测中具有一定的使用价值。
This paper proposes a geographical object-based method for change detection with high resolution images based on the changing areas distributed as a clustered type. This algorithm utilizes the Mean-Shift segmentation algorithm to extract a geographic object, and then uses the gray information of the geographic object with the EM algorithm to automatically extract changed and unchanged areas. This method considers spatial neighborhood information which can avoid the isolation and discrete disconnected areas in change results when using a pixel-based method. This method also reduces intervention when determining the change threshold value. Groups of three different spatial resolution images （ QuickBird, SPOT, TM images） are used to verify this proposed geographic object-based change detection algorithm and compared the accuracy and precision with a pixel-base method. Our results show that the accuracy with object-based change detection method on QuickBird, SPOT and TM images was 91. 1%, 87. 3% and 84. 3%, while for the pixel-based method are 86. 41%,82. 48% and 81.02% respectively. These results illustrate that the object-based change detection method has higher change detection accuracy than the pixel based approach. Moreover, the object-based method has better accuracy for high spatial resolution than in middle or low resolution images.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2014年第8期906-912,共7页
Geomatics and Information Science of Wuhan University
基金
国家863计划资助项目(2012AA12A304)
中央高校基本科研业务费专项资金资助项目(2012ZYTS037)~~
关键词
变化检测
影像分割
地理对象
MEAN-SHIFT
EM
change detection
image segment
geographic object-based
Mean-Shift
expectation Max- imization（EM）
