The high spatial resolution remotely sensed imagery has abundant detailed information of earth surface ,and the multi‐temporal change detection for the high resolution remotely sensed imagery can realize the variations of geographical unit .In terms of the high spatial resolution remotely sensed imagery ,the traditional remote sensing change detection algorithms have ob‐vious defects .In this paper ,learning from the object‐based image analysis idea ,we proposed a semi‐automatic threshold selec‐tion algorithm named OB‐HMAD (object‐based‐hybrid‐MAD) ,on the basis of object‐based image analysis and multivariate alter‐native detection algorithm (MAD) .which used the spectral features of remotely sensed imagery into the field of object‐based change detection .Additionally ,OB‐HMAD algorithm has been compared with other the threshold segmentation algorithms by the change detection experiment .Firstly ,we obtained the image object by the multi‐solution segmentation algorithm .Secondly , we got the object‐based difference image object using MAD and minimum noise fraction rotation (MNF) for improving the SNR of the image object .Then ,the change objects or area are classified using histogram curvature analysis (HCA) method for the semi‐automatic threshold selection ,which determined the threshold by calculated the maximum value of curvature of the histo‐gram ,so the HCA algorithm has better automation than other threshold segmentation algorithms .Finally ,the change detection results are validated using confusion matrix with the field sample data .Worldview‐2 imagery of 2012 and 2013 in case study of Beijing were used to validate the proposed OB‐HMAD algorithm .The experiment results indicated that OB‐HMAD algorithm which integrated the multi‐channel spectral information could be effectively used in multi‐temporal high resolution remotely sensed imagery change detection ,and it has basically solved the“salt and pepper”problem which always exists in the pixel‐based change detection ,and has mitigated the impact of building shadows and geometric registration error ,and has improved the over‐all accuracy and kappa coefficient than other change detection algorithm ,but it has more undetected error .By compared with the SNR of image object ,we know that the MNF transformation could effectively improve to concentrate the change information .%高空间分辨率遥感影像蕴涵丰富的地物细节信息，针对高分辨率多时相遥感影像的变化检测可以更清楚认识到地理单元的变化情况，传统的遥感变化检测算法面对高分辨率遥感影像时，会出现明显的＂椒盐现象＂。本文借鉴面向对象图像分析的思想，以高分辨率遥感影像对象的光谱特征为分析对象，在多变量变化检测算法（multivariate alternative detection ，MAD）的基础上，提出一种半自动阈值选取的OB‐HMAD （object based‐hybrid MAD）算法，并利用该算法进行变化检测实验对比分析。首先对高分辨率多时相遥感影像进行多尺度分割，形成多通道的影像对象；其次利用MAD变换，形成差异影像对象，并对其进行MNF变换，提高影像对象的信噪比；然后采用直方图曲率分析（histogram curvature analysis ，HCA）进行半自动阈值选取，提取变化区域；最后结合实地样本数据对变化检测结果进行混淆矩阵的精度验证。结合2012年和2013年北京地区Worldview‐2影像的实验可知，OB‐HM AD算法融合多通道的光谱信息，可以有效的实现多时相高分影像的变化检测，基本消除了基于像元变化检测中＂椒盐＂现象的干扰，并在一定程度上降低建筑物阴影和几何配准误差的影响，总体精度和kappa系数也较优于其他变化检测算法，但存在较大的漏检误差。MNF变换可以有效的提高影像的信噪比，使差异信息更集中，直方图曲率分析的阈值分割算法相对其他阈值算法，自动化程度更高。
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