A Fast Sequential Similarity Detection Algorithm for Multi-Source Image Matching

Robust and efficient multi-source image matching remains a challenging task due to nonlinear radiometric differences between image features. This paper proposes a pixel-level matching framework for multi-source images to overcome this issue. Firstly, a novel descriptor called channel features of phase congruency (CFPC) is first derived at each control point to create a pixelwise feature representation. The proposed CFPC is not only simple to construct but is also highly efficient and somewhat insensitive to noise and intensity changes. Then, a Fast Sequential Similarity Detection Algorithm (F-SSDA) is proposed to further improve the matching efficiency. Comparative experiments are conducted by matching different types of multi-source images (e.g., Visible–SAR; LiDAR–Visible; visible–infrared). The experimental results demonstrate that the proposed method can achieve pixel-level matching accuracy with high computational efficiency.