An accurate and efficient image registration algorithm in the aerial infrared images

Abstract

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle (UAV) is more and more utilized to capture the aerial images. Therein, the registration of the aerial infrared images play a vital role in the infrared image processing, such as object tracking, information collecting, and behavior analysis. However, due to the low contrast, low-resolution, and few texture features of the infrared images, it is really difficult to implement the image registration in the aerial infrared images. In our work, an accurate and efficient image registration algorithm in the aerial infrared images is put forward. To improve the registration accuracy and efficiency, we construct multiple screening mechanisms to screen out the incorrect and redundant feature points. Besides, to further improve the registration accuracy, the improved SIFT and convolution neural network (CNN) descriptors are combined for the feature description. Finally, the coarse-to-fine feature matching strategy is put forward via FLANN and RANSAC algorithms. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive registration results.