Automatic georeferencing of imagery from high-resolution, low-altitude, low-cost aerial platforms

Existing nadir-viewing aerial image databases such as that available on Google Earth contain data from a variety of sources at varying spatial resolutions. Low-cost, low-altitude, high-resolution aerial systems such as unmanned aerial vehicles and balloon-borne systems can provide ancillary data sets providing higher resolution, obliquelooking data to enhance the data available to the user. This imagery is difficult to georeference due to the different projective geometry present in these data. Even if this data is accompanied by metadata from global positioning system (GPS) and inertial measurement unit (IMU) sensors, the accuracy obtained from low-cost versions of these sensors is limited. Combining automatic image registration techniques with the information provided by the IMU and onboard GPS, it is possible to improve the positioning accuracy of these oblique data sets on the ground plane using existing orthorectified imagery available from sources such as Google Earth. Using both the affine scale-invariant feature transform (ASIFT) and maximally stable extremal regions (MSER), feature detectors aid in automatically detecting correspondences between the obliquely collected images and the base map. These correspondences are used to georeference the high-resolution, oblique image data collected from these low-cost aerial platforms providing the user with an enhanced visualization experience.