Compressed Binary Discriminative Feature for Fast UAV Image Registration

Efficiently UAV images mosaicking is of critical importance for the application of disaster management, in which fast image registration plays an important role. Towards fast and accurate image registration, the key design lies in the keypoint description, to which end SIFT and SURF are widely leveraged in the related literature. However, the expensive computation and memory costs restrict their potential in disaster management. In this paper, we proposed a novel keypoint descriptor termed CBDF (Compressed Binary Discriminative Feature). A cascade of binary strings is computed by efficiently comparing image gradients static information over a log-polar location grid pattern. Extensive evaluations on benchmark datasets and real-world UAV images show that CBDF yields a similar performance with SIFT and SURF, and it is much more efficient in terms of both computation time and memory.