Evaluating the applicability of BRISK for the geometric registration of remote sensing images

Abstract

Binary Robust Invariant Scalable Keypoints (BRISK) is one of several relatively new matching algorithms aiming to improve well-established algorithms such as Scale-Invariant Feature Transform (SIFT) or Speeded-Up Robust Features. A detailed evaluation of the BRISK applicability for geometric registration of remote sensing images is performed. As the original algorithm was not developed with a focus on remote sensing image matching, a practical processing chain for the image registration of a newly acquired image with a reference image was developed. This chain also includes a modified Random Sample Consensus outlier removal based on the sensor-model of the to-be-registered image. The presented methodology is evaluated and compared to the SIFT operator in terms of repeatability, accuracy, recall and precision. Our results show that BRISK performs very well on remote sensing images and together with the sensor-model-based outlier removal offers a significant improvement over existing image registration methods such as SIFT.