摘要
Subpixel localization in image center is one of the key technologies of vision measurement. In order to meet the requirements of accurate calibration and measurement in multi-field, the existing sub-pixel positioning methods are complex, the positioning accuracy is greatly affected by the effect of initial edge extraction, and the positioning accuracy is low. Because remote sensing multi-view images are usually not stationary random signals, in order to better express the non-stationary characteristics of images, random analysis is combined to segment sub-pixel objects in the center of remote sensing images. The accuracy of mark positioning will affect the accuracy of the whole measurement. The control point signs with different characteristics correspond to different recognition methods, so the selection of control point marks should be based on different requirements. It is used to describe the target view from different viewpoints and use the geometric features to retrieve the model library. The matching process uses global and local, statistical and structural target recognition features hierarchically, and is divided into two steps of retrieval and exact matching. The experiment was carried out to verify the effectiveness of the method.
