CAJ | 학술논문

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。
고공간분변솔요감영상온함봉부적지물세절신식，침대고분변솔다시상요감영상적변화검측가이경청초인식도지리단원적변화정황，전통적요감변화검측산법면대고분변솔요감영상시，회출현명현적＂초염현상＂。본문차감면향대상도상분석적사상，이고분변솔요감영상대상적광보특정위분석대상，재다변량변화검측산법（multivariate alternative detection ，MAD）적기출상，제출일충반자동역치선취적OB‐HMAD （object based‐hybrid MAD）산법，병이용해산법진행변화검측실험대비분석。수선대고분변솔다시상요감영상진행다척도분할，형성다통도적영상대상；기차이용MAD변환，형성차이영상대상，병대기진행MNF변환，제고영상대상적신조비；연후채용직방도곡솔분석（histogram curvature analysis ，HCA）진행반자동역치선취，제취변화구역；최후결합실지양본수거대변화검측결과진행혼효구진적정도험증。결합2012년화2013년북경지구Worldview‐2영상적실험가지，OB‐HM AD산법융합다통도적광보신식，가이유효적실현다시상고분영상적변화검측，기본소제료기우상원변화검측중＂초염＂현상적간우，병재일정정도상강저건축물음영화궤하배준오차적영향，총체정도화kappa계수야교우우기타변화검측산법，단존재교대적루검오차。MNF변환가이유효적제고영상적신조비，사차이신식경집중，직방도곡솔분석적역치분할산법상대기타역치산법，자동화정도경고。
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 .