Change Detection Based on Multi-scale Geometric Feature Vector

We present a change detection method based on multi-scale geometric feature vector(MSG-FV).The change analysis standard in this novel method is converted from pixel spectral space to seg- ment scale space.Context information beyond multi-scale imagery segmentation is applied to performchange detection.Specifically,this approach specifies number and values of multiple segmentationscales at first.Secondly,two-date imageries are segmented respectively using multiple scales.Third-ly,multi-scale geometric feature vector of a detection unit are constructed in different dates.Finally, 第40卷第5期陆 苗等:利用多尺度几何特征向量的变化检测方法627the change intensity between two-date multi-scale geometric feature vectors is calculated as the changestandard.The study area of Weinan in Shannxi province is tested to analyze the land cover changefrom 2000to 2009.In this sample area,three geometric features are used in this approach.Then,theoptimized geometric feature is compared to other existing methods(CVA,Correlation).The MSGFVapproach is proved to outperform other methods.