Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of feature descriptors. However, the existing feature descriptors confront several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposed a feature descriptor for image stitching, denoted as Lightweight Multi-Feature Descriptors (LMFD). By extensively extracting gradients, means, and global information surrounding the feature points, the feature descriptors are generated through various combinations to enhance the image stitching. This empowers the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0 and 1, which not only facilitates more efficient hardware deployment but also enhances computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of the LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrated that the LMFD outperformed state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of the LMFD and substantiates its potential for practical applications in various domains.