Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.