UAV aerotriangulation with flight-control data support

With the development of UAV, it can get a lot of remote sensing data. In high risk situations and inaccessible areas, it is a problem to quickly mosaic the high resolution image to a single image. UAVs, especially low-cost UAVs, limit the sensor payload in weight and dimension, so that often low weight sensors like small or medium format amateur cameras are selected. Therefore, in comparison to large format cameras, UAVs have to acquire a higher number of images in order to obtain the same image coverage and comparable image resolution. Moreover, low-cost sensors are normally less stable than high-end sensors, which results in a reduced image quality. In addition, these payload limitations require the use of low weight navigation units, which implies less accurate results for the orientation of the sensors. In this article, using sift algorithm to extract feature point, using kd-tree algorithm to match the same point between relative images. The flight-control data is used as geodetic observation for combined bundle adjustment.