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Automated Generation of a Digital Elevation Model Over Steep Terrain in Antarctica From High-Resolution Satellite Imagery

机译:利用高分辨率卫星图像自动生成南极陡峭地形上的数字高程模型

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The automated generation of a digital elevation model over the Antarctic using stereo matching high-resolution satellite images is a challenging task. Moreover, the homogeneous radiometry in the icy environment and the strong geometric dissimilarity between stereo pairs over the steep terrain limit the use of area-based matching techniques. To overcome this issue, we propose template matching with image transformation in order to reduce the geometric dissimilarity. First, we generated epipolar resampled images to ensure the ease of estimation and handling of image dissimilarities from various viewing directions. We then utilized the normalized cross-correlation (NCC) and transformed the image patches within the matching window along the sample, line, and diagonal directions in order to improve the match rates within the steep areas. Furthermore, we tested the proposed method using Antarctic IKONOS stereo images and found that the overall matching success rate improved from 93.5% to 97.0% for all image pixels. We then computed the success rates over an area in which the NCC produced a low elevation point density and observed a more significant improvement from 58.7% to 79.26%. When compared to the manually generated elevation, the maximum vertical difference improved from 11.4 to 4.7 m. With these improvements, we can build a 1-m resolution elevation model over the glaciated high relief terrain.
机译:使用立体声匹配高分辨率卫星图像在南极上自动生成数字高程模型是一项艰巨的任务。此外,在冰冷环境中的均匀辐射测量以及陡峭地形上的立体对之间的强烈几何差异限制了基于区域的匹配技术的使用。为了克服这个问题,我们提出了模板匹配和图像变换,以减少几何差异。首先,我们生成了对极重采样图像,以确保易于估计和处理来自各个查看方向的图像差异。然后,我们利用归一化互相关(NCC)并沿着样本,直线和对角线方向对匹配窗口内的图像块进行了变换,以提高陡峭区域内的匹配率。此外,我们使用南极IKONOS立体图像测试了该方法,发现所有图像像素的整体匹配成功率从93.5%提高到97.0%。然后,我们计算了NCC产生低海拔点密度的区域的成功率,并观察到从58.7%到79.26%的更大改善。与手动生成的高程相比,最大垂直差从11.4提高到4.7 m。通过这些改进,我们可以在冰川高浮雕地形上建立分辨率为1-m的高程模型。

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