The objective of this research is to develop an approach to correct nonlinear errors in the SRTM (Shuttle Radar Topography Mission) elevations, which cannot be handled by most traditional methods. First, a set of uncorrelated feature attributes has been generated from the SRTM digital elevation model (DEM) together with the new freely available Sentinel-2 multispectral imagery, over a dense urban area in Egypt. Second, the SRTM DEM, Sentinel-2 image, and the generated attributes have been applied as input data in an artificial neural network (ANN) classification model to assign each pixel to each of 12 reference elevations. Finally, the posterior probabilities obtained for ANN have been combined based on an inverse probability weighted interpolation (IPWI) approach to estimate revised SRTM elevations. The results were compared with a reference DEM with 1-m vertical accuracy derived through image matching of the Worldview-1 stereo satellite imagery. The process of performance evaluation is based on various statistics such as scatter plots, correlation coefficient (R), standard deviation (SD), and root mean square error (RMSE). The results show that, using the SRTM DEM as a single data source, the RMSE of estimated elevations has improved to 3.04 m. On the other hand, including the Sentinel-2 image has improved the RMSE of elevations to 2.93 m. Including the generated attributes as well has improved the estimated RMSE of the elevations to 2.07 m. Compared with the results from the commonly used multiple linear regression (MLR) method, the improvement in RMSE of the estimated elevations can reach 45%.

这项研究的目的是开发一种纠正SRTM（航天飞机雷达地形任务）中的非线性误差的方法。高程，这是大多数传统方法无法处理的。首先，在埃及人口稠密的城市地区，根据SRTM数字高程模型（DEM）以及新的可免费获得的Sentinel-2多光谱图像，生成了一组不相关的要素属性。其次，SRTM DEM，Sentinel-2图像和生成的属性已被用作人工神经网络（ANN）分类模型中的输入数据，以将每个像素分配给12个参考高程中的每一个。最后，已基于逆概率加权插值（IPWI）方法对ANN获得的后验概率进行了组合，以估计修订后的SRTM高程。将结果与通过Worldview-1立体声卫星图像的图像匹配得出的垂直精度为1-m的参考DEM进行比较。R），标准差（SD）和均方根误差（RMSE）。结果表明，使用SRTM DEM作为单个数据源，估计高程的均方根误差（RMSE）已提高到3.04 m。另一方面，包括Sentinel-2影像已将高程的RMSE提高到2.93 m。包括生成的属性在内，还将海拔的估计RMSE提高到2.07 m。与常用的多元线性回归（MLR）方法得出的结果相比，估计海拔的RMSE改善可以达到45％。