LPHOG: A Line Feature and Point Feature Combined Rotation Invariant Method for Heterologous Image Registration

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.

Keywords

• heterologous image matching
• line features
• point features
• LSD algorithm
• HOG
• FSC algorithm