Change detection based on similarity measurement of object histogram using high-resolution remote sensing imagery

LI Liang; GONG Yan; LI Xue; WANG Kai

doi:10.11834/jrs.20133009

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Change detection based on similarity measurement of object histogram using high-resolution remote sensing imagery
Vol. 18, Issue 1, Pages: 139-153(2014)
Published：2014
DOI： 10.11834/jrs.20133009
稿件说明：

移动端阅览

[1]李亮,龚龑,李雪,王凯.像斑直方图相似性测度的高分辨率遥感影像变化检测[J].遥感学报,2014,18(01):139-153. DOI： 10.11834/jrs.20133009.

LI Liang, GONG Yan, LI Xue, et al. Change detection based on similarity measurement of object histogram using high-resolution remote sensing imagery[J]. Journal of Remote Sensing, 2014, 18(1): 139-153. DOI： 10.11834/jrs.20133009.

摘要

基于像斑的变化向量分析法CVA（Change Vector Analysis）过分依赖像斑的灰度均值信息

而未能有效利用其灰度分布信息

这在高分辨率遥感影像变化检测中存在不足。本文提出了一种基于像斑直方图相似性测度的变化检测方法。利用G统计量构建不同时期像斑之间的相似性测度。假设所有像斑的相似性测度值符合混合高斯分布模型

通过期望最大化算法EM（Expectation Maximization）求解相关参数

最后采用基于最小错误率的贝叶斯判别规则获取最终的变化结果。实验表明

本文提出的上述方法能够有效提高变化检测的精度。

Abstract

The object-oriented change vector analysis method

which is excessively dependent on the mean value of each object but failed to use gray distribution information

is deficient in change detection using high-resolution remote sensing images. A new method introducing similarity measurement of object histogram is proposed in this study. First

the similarity measurement of o bjects between different periods is built up by G statistic. Second

the Expectation Maximization（ EM） algorithm is used to calculate the related parameters according to the assumption that all similarity measurement values of objects fit a Gaussian Mixture D istribution model. Finally

the Bayesian rule with minimum error rate is applied to get the change / no change results. Experimental results show that the method can get results with higher accuracy in change detection

especially for high-resolution remote sensing images.