Advances in DSM generation and higher level information extraction from high resolution optical stereo satellite data

The automatic generation of digital surface models (DSM) of urban areas from high and very high resolution (VHR) stereo data from satellites is still a research issue. Image matching algorithms from computer vision have been introduced and adopted to satellite imagery in recent years. These algorithms do not work using local optimisation like area based matching but try to optimize a global cost function. Analysis shows that matching approaches based on epipolar images like semi-global matching (SGM) and new methods using total generalized variation (TGV) yield the best results. Especially satellites like Worldview-2, GeoEye-1 and Pleiades exhibit very high spatial resolution and geometric quality and can therefore be used to generate DSMs with good properties. If several stereo images from one orbit are available, a combination of DSMs generated by different stereo pairs leads to even better results. Using these DSMs, which already show urban area features in high detail, further higher level information can be extracted and new products can be generated.利用卫星获取的高分辨率及超高分辨率（Very High Resolution, VHR）立体影像数据自动生成城市区域数字表面模型（Digital Surface Model, DSM）仍是一项待研究的课题。近年来，计算机视觉领域的影像匹配算法被引入并应用于卫星影像处理。此类算法摒弃了基于区域匹配的局部优化思路，转而致力于优化全局代价函数。研究分析显示，基于核线影像的匹配方法（如半全局匹配（Semi-Global Matching, SGM））以及采用总广义变分（Total Generalized Variation, TGV）的新型算法均可取得最优匹配效果。尤其是Worldview-2、GeoEye-1与Pleiades等卫星，拥有极高的空间分辨率与几何成像质量，可用于生成性能优良的数字表面模型。若可获取单条轨道下的多幅立体影像，将不同立体像对生成的数字表面模型进行融合，可获得更优的模型效果。依托这些已具备高细节城市区域特征的数字表面模型，可进一步提取更高层级的地理信息，并生成新型衍生产品。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于从高分辨率光学立体卫星数据中生成数字表面模型（DSM）的进展，特别是针对城市区域，介绍了半全局匹配（SGM）和总广义变分（TGV）等先进图像匹配算法的应用。它利用Worldview-2、GeoEye-1和Pleiades等高分辨率卫星数据，通过组合多个立体图像对提升DSM质量，并支持进一步提取更高层次信息以生成新产品。