Affiliation:
1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. BYD Auto Industry Company Ltd., Shenzhen 518118, China
Abstract
Remote sensing image registration has been a very important research topic, especially the registration of heterologous images. In the research of the past few years, numerous registration algorithms for heterogenic images have been developed, especially feature-based matching algorithms, such as point feature-based or line feature-based matching methods. However, there are few matching algorithms that combine line and point features. Therefore, this study proposes a matching algorithm that combines line features and point features while achieving good rotation invariance. It comprises LSD detection of line features, keypoint extraction, and HOG-like feature descriptor construction. The matching performance is compared with state-of-the-art matching algorithms on three heterogeneous image datasets (optical–SAR dataset, optical–infrared dataset, and optical–optical dataset), verifying our method’s rotational invariance by rotating images in each dataset. Finally, the experimental results show that our algorithm outperforms the state-of-the-art algorithms in terms of matching performance while possessing very good rotation invariance.
Funder
Science and Technology Department of Jilin Province, China
Subject
General Earth and Planetary Sciences