利用影像相关分析的遥感变化检测

针对利用单一光谱特征进行影像相关分析在遥感变化检测应用中效果较差的问题，提出了一种将纹理特征与光谱特征相结合的相关系数计算模型。应用中在对多时相影像进行相同尺度棋盘分割的基础上，先计算各对应分割窗口内的相关系数，再以其中心点的坐标和相关系数值作为一个特征点，在三维空间中进行插值处理得到整个区域的相关系数空间分布图；进一步通过密度分割处理提取变化信息。本文以两期GF-1影像数据进行了变化信息提取试验，结果表明，采用组合特征相关系数的变化检测结果明显优于单一光谱相关系数的变化检测结果。组合相关系数的应用研究为利用影像相关分析方法从高分影像中提取变化信息提供了一种新的思路。

Remote sensing change detection using image correlation analysis

Aiming at the problem that image correlation analysis using single spectral features is poor in remote sensing change detection applications, a correlation coefficient calculation model combining texture features and spectral features is proposed. Firstly, the multi-temporal image is segmented by the same scale, and the correlation coefficients in each corresponding segmentation window are calculated. Then the coordinates of the center point of the window and the correlation coefficient value are taken as a feature point, and the spatial distribution of the correlation coefficient of the whole region is obtained by interpolation in the three-dimensional space. Finally, the change information is extracted by density slicing. The paper carries out the change information extraction experiment with two GF-1 image data. The results show that the change detection result of the combined feature correlation coefficient is obviously better than the single spectral correlation coefficient change detection result. The application research of combined correlation coefficient provides a new idea for remote sensing change information extraction using correlation analysis method.