For real-world simulation, terrain models must combine various types of information on material and texture in terrain reconstruction for the three-dimensional numerical simulation of terrain. However, the construction of such models using the conventional method often involves high costs in both manpower and time. Therefore, this study used a convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify the construction of future models. Visible light (i.e., RGB), near infrared (NIR), normalized difference vegetation index (NDVI), and digital surface model (DSM) images were examined.

This paper proposes the use of the robust U-Net (RUNet) model, which integrates multiple CNN architectures, for material classification. This model, which is based on an improved U-Net architecture combined with the shortcut connections in the ResNet model, preserves the features of shallow network extraction. The architecture is divided into an encoding layer and a decoding layer. The encoding layer comprises 10 convolutional layers and 4 pooling layers. The decoding layer contains four upsampling layers, eight convolutional layers, and one classification convolutional layer. The material classification process in this study involved the training and testing of the RUNet model. Because of the large size of remote sensing images, the training process randomly cuts subimages of the same size from the training set and then inputs them into the RUNet model for training. To consider the spatial information of the material, the test process cuts multiple test subimages from the test set through mirror padding and overlapping cropping; RUNet then classifies the subimages. Finally, it merges the subimage classification results back into the original test image.

The aerial image labeling dataset of the National Institute for Research in Digital Science and Technology (Inria, abbreviated from the French Institut national de recherche en sciences et technologies du numérique) was used as well as its configured dataset (called Inria-2) and a dataset from the International Society for Photogrammetry and Remote Sensing (ISPRS). Material classification was performed with RUNet. Moreover, the effects of the mirror padding and overlapping cropping were analyzed, as were the impacts of subimage size on classification performance. The Inria dataset achieved the optimal results; after the morphological optimization of RUNet, the overall intersection over union (IoU) and classification accuracy reached 70.82% and 95.66%, respectively. Regarding the Inria-2 dataset, the IoU and accuracy were 75.5% and 95.71%, respectively, after classification refinement. Although the overall IoU and accuracy were 0.46% and 0.04% lower than those of the improved fully convolutional network, the training time of the RUNet model was approximately 10.6h shorter. In the ISPRS dataset experiment, the overall accuracy of the combined multispectral, NDVI, and DSM images reached 89.71%, surpassing that of the RGB images. NIR and DSM provide more information on material features, reducing the likelihood of misclassification caused by similar features (e.g., in color, shape, or texture) in RGB images. Overall, RUNet outperformed the other models in the material classification of remote sensing images. The present findings indicate that it has potential for application in land use monitoring and disaster assessment as well as in model construction for simulation systems.

对于真实世界的模拟，地形模型必须结合地形重建中的各种类型的材料和纹理信息，以进行地形的三维数值模拟。然而，使用传统方法构建此类模型通常涉及高昂的人力和时间成本。因此，本研究使用卷积神经网络 (CNN) 架构对多光谱遥感图像中的材料进行分类，以简化未来模型的构建。检查了可见光（即RGB）、近红外（NIR）、归一化差异植被指数（NDVI）和数字表面模型（DSM）图像。

本文提出了使用鲁棒的 U-Net (RUNet) 模型进行材料分类，该模型集成了多个 CNN 架构。该模型基于改进的 U-Net 架构并结合 ResNet 模型中的快捷连接，保留了浅层网络提取的特征。该架构分为编码层和解码层。编码层包括 10 个卷积层和 4 个池化层。解码层包含四个上采样层、八个卷积层和一个分类卷积层。本研究中的材料分类过程涉及 RUNet 模型的训练和测试。由于遥感影像尺寸较大，训练过程从训练集中随机剪下相同大小的子图像，然后输入到 RUNet 模型中进行训练。考虑材料的空间信息，测试过程通过镜像填充和重叠裁剪从测试集中切出多个测试子图像；然后 RUNet 对子图像进行分类。最后，它将子图像分类结果合并回原始测试图像。

使用了国家数字科学与技术研究所（Inria，缩写自法国国立科学与技术研究所）的航空图像标记数据集及其配置的数据集（称为 Inria-2）和一个来自国际摄影测量和遥感学会 (ISPRS) 的数据集。使用 RUNet 进行材料分类。此外，分析了镜像填充和重叠裁剪的影响，以及子图像大小对分类性能的影响。Inria数据集达到了最优结果；经过 RUNet 的形态优化，整体交集（IoU）和分类准确率分别达到了 70.82% 和 95.66%。对于 Inria-2 数据集，IoU 和准确率分别为 75.5% 和 95.71%，分别在分类细化后。虽然整体 IoU 和准确率比改进的全卷积网络低 0.46% 和 0.04%，但 RUNet 模型的训练时间缩短了约 10.6h。在ISPRS数据集实验中，组合多光谱、NDVI和DSM图像的整体精度达到89.71%，超过RGB图像。NIR 和 DSM 提供了更多关于材料特征的信息，减少了由 RGB 图像中的相似特征（例如，颜色、形状或纹理）引起的错误分类的可能性。总体而言，RUNet 在遥感图像的材料分类方面优于其他模型。目前的研究结果表明，它具有应用于土地利用监测和灾害评估以及模拟系统模型构建的潜力。分类细化后。虽然整体 IoU 和准确率比改进的全卷积网络低 0.46% 和 0.04%，但 RUNet 模型的训练时间缩短了约 10.6h。在ISPRS数据集实验中，组合多光谱、NDVI和DSM图像的整体精度达到89.71%，超过RGB图像。NIR 和 DSM 提供了更多关于材料特征的信息，减少了由 RGB 图像中的相似特征（例如，颜色、形状或纹理）引起的错误分类的可能性。总体而言，RUNet 在遥感图像的材料分类方面优于其他模型。目前的研究结果表明，它具有应用于土地利用监测和灾害评估以及模拟系统模型构建的潜力。分类细化后。虽然整体 IoU 和准确率比改进的全卷积网络低 0.46% 和 0.04%，但 RUNet 模型的训练时间缩短了约 10.6h。在ISPRS数据集实验中，组合多光谱、NDVI和DSM图像的整体精度达到89.71%，超过RGB图像。NIR 和 DSM 提供了更多关于材料特征的信息，减少了由 RGB 图像中的相似特征（例如，颜色、形状或纹理）引起的错误分类的可能性。总体而言，RUNet 在遥感图像的材料分类方面优于其他模型。目前的研究结果表明，它具有应用于土地利用监测和灾害评估以及模拟系统模型构建的潜力。