Arctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To overcome the challenges, the proposed method introduces four improvements to the object-space-based image-matching pipeline: relative georeferencing to recover the horizontality and scale of sea-ice DSMs using a terrestrial light detection and ranging (LiDAR) dataset, match inspection to verify the matched points using several matching constraints, adaptive search-window adjustment to ensure distinct texture information through simple texture analysis, and robust vertical positioning to reduce the matching uncertainty via matching-indicator modeling. Performance evaluations were conducted with drone and LiDAR datasets obtained from a sea-ice campaign using the Korean Icebreaker Research Vessel (IBRV) Araon in the summer of 2017. The experimental results indicated that the proposed method can achieve significant quality enhancements compared with the existing matching method and that all the considerations contributed significantly to the enhancements.

北极海冰不断移动并覆盖着低纹理的表面，因此很难从无人机图像生成可靠的数字表面模型（DSM）。海冰的移动使DSM的地理配准变得困难，而低纹理的海冰表面会导致图像匹配的不确定性。本文提出了一种鲁棒的方法来生成用于漂移海冰的高质量DSM。为了克服这些挑战，该方法对基于对象空间的图像匹配管道进行了四项改进：使用地面光检测和测距（LiDAR）数据集进行相对地理配准以恢复海冰DSM的水平度和比例，进行匹配检查使用几个匹配约束条件来验证匹配点，自适应搜索窗口调整，以通过简单的纹理分析来确保不同的纹理信息，稳健的垂直定位，以通过匹配指标建模减少匹配不确定性。使用2017年夏季使用韩国破冰船研究船（IBRV）Araon从海冰战役中获得的无人机和LiDAR数据集进行了性能评估。实验结果表明，与现有匹配方法相比，该方法可以显着提高质量方法，并且所有考虑因素都对增强功能做出了重大贡献。