Evaluating automatic registration of UAV imagery using multi-temporal ortho images

摘要

Accurate geo-registration of acquired imagery is an important task when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. As an example, change detection needs accurately geo-registered images for selecting and comparing co-located images taken at different points in time. One challenge using small UAVs lies in the instable flight behavior and using low-weight cameras. Thus, there is a need to stabilize and register the UAV imagery by image processing methods since using only direct approaches based on positional information coming from a GPS and attitude and acceleration measured by an inertial measurement unit (IMU) are not accurate enough. In order to improve this direct geo-registration (or "pre-registration"), image matching techniques are applied to align the UAV imagery to geo-registered reference images. The main challenge consists in matching images taken from different sensors at different day time and seasons. In this paper, we present evaluation methods for measuring the performance of image registration algorithms w.r.t. multi-temporal input data. They are based on augmenting a set of aligned image pairs by synthetic pre-registrations to an evaluation data set including truth transformations. The evaluation characteristics are based on quantiles of transformation residuals at certain control points. For a test site, video frames of a UAV mission and several ortho images from a period of 12 years are collected and synthetic pre-registrations corresponding to real flight parameters and registration errors are computed. Two algorithms A1 and A2 based on extracting key-points with a floating point descriptor (A1) and a binary descriptor (A2) are applied to the evaluation data set. As evaluation result, the algorithm A1 turned out to perform better than A2. Using affine or Helmert transformation types, both algorithms perform better than in the projective case. Furthermore, the evaluation classifies the ortho images w.r.t. their degree of difficulty and even for the most unfavorable ortho image, the evaluation characteristics yield better results than those attached to the default pre-registration. Finally, the proposed evaluation methods have been proven to derive valuable results even for input data with a high degree of difficulty.