UTSFANet: Unsupervised Two-Stage Fine Adjustment Network for Infrared Remote Sensing Image Stitching

Abstract

Image stitching aims to align two images from different perspectives. For infrared remote sensing images, the low resolution, lack of strong-feature points, and the presence of large textureless regions make it difficult to achieve effective feature matching and high-quality image stitching results. In the field of remote sensing stitching, the primary challenge is how to effectively extract features, reduce the influence of parallax, and improve the registration accuracy. To improve the image stitching performance and obtain parallax-tolerant fine registration results, we propose a two-stage image stitching method based on unsupervised learning. First, in the first stage, we use a multilevel feature extraction network to effectively extract image correlation features, progressively refining the registration from coarse to fine, thus ensuring performance under large-baseline conditions. Second, by utilizing a discrete-feature detection module in the multilevel network, we remove anomalous feature regions and recombine effective local feature regions, enabling the fusion of detailed features with global features and improving registration accuracy. Finally, in the second stage, an image fine adjustment module is applied to process the image background and foreground, further eliminating parallax artifacts and improving registration accuracy. Compared with the existing methods, our method has advantages in both registration accuracy and parallax tolerance. Extensive experiments demonstrate that our method effectively registers and stitches infrared remote sensing images on both the self-built infrared remote sensing dataset and the publicly available UDIS-D dataset, outperforming current state-of-the-art methods in terms of performance.