利用矢量影像法进行土地利用变化自动检测

为解决土地利用矢量图与遥感影像的变化检测问题，提出了一种基于类别的矢量图与遥感影像变化检测方法。在矢量图约束下，对遥感影像进行影像分割获取像斑；提取像斑在遥感影像上的直方图特征，采用G统计量度量像斑之间的特征距离；利用像斑与其他相同类别像斑之间的特征距离，构建单波段上像斑的类别异质度，自适应加权组合各波段上像斑的类别异质度构建像斑的类别异质度；依据最大熵方法获取各地物类别对应的异质度阈值，以类别为单位对各像斑进行变化判别，获取变化检测结果。在QuickBird遥感影像上的试验验证了本文方法的有效性，实现了矢量图与遥感影像的自动变化检测。

Class-based Change Detection Method Using Vector Map and Remote Sensing Imagery

In order to solve the land use change detection problem in vector map and remote sensing imagery, this paper puts forward a change detection method of vector map and remote sensing imagery based on category.The remote sensing image segmentation is carried out to obtain image patches under the constraint of vector map.Then the histogram features of remote sensing imagery patches are extracted and the feature distance between image patches is measured by using G statistic.Thus the class heterogeneity of image patches on single wave is constructed by using the feature distance between image patches and other similar image patches, adaptive weighting combines the class heterogeneity of image patches on each band to establish the class heterogeneity of image patches.Then according to the max entropy method, the threshold of the corresponding heterogeneity of each object category is obtained and the change verification is applied based on category classification to get the change information.The proposed method's effectiveness is verified by the experimental results on the QuickBird images and it realizes the automatic change detection of vector map and remote sensing imagery.