[1]韩超,张晓滨*.基于多尺度残差增强网络的DEM超分辨率重建[J].计算机技术与发展,2025,(03):9-17.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0319]
　HAN Chao,ZHANG Xiao-bin*.DEM Super-resolution Reconstruction Based on Multi-scale Residual Enhancement Network[J].,2025,(03):9-17.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0319]
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基于多尺度残差增强网络的DEM超分辨率重建()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年03期
页码:
9-17
栏目:
媒体计算
出版日期:
2025-03-10

文章信息/Info

Title:
DEM Super-resolution Reconstruction Based on Multi-scale Residual Enhancement Network
文章编号:
1673-629X(2025)03-0009-09
作者:
韩超; 张晓滨*
西安工程大学 计算机科学学院,陕西 西安 710699
Author(s):
HAN Chao; ZHANG Xiao-bin*
School of Computer Science,Xi’an Polytechnic University,Xi’an 710699,China
关键词:
数字高程模型; 超分辨率重建; 多尺度; 残差融合网络; 多尺度通道注意力; 可变形卷积
Keywords:
digital elevation model; super-resolution reconstruction; multi-scale; residual fusion network; multi-scale channel attention;  deformable convolution
分类号:
TP391
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0319
摘要:
数字高程模型(DEM)被认为是最重要的基础地理数据模型之一,在水文分析、路径规划和建模等方面有着广泛的应用。 然而,通过具有更高精度的传感器获取大面积高分辨率 DEM 数据的高成本对许多地理分析应用提出了挑战。 结合多尺度特征、残差学习和多尺度通道注意力机制,该文提出了基于多尺度残差多通道注意力增强网络的数字高程模型超分辨率重建方法,其中多尺度残差多通道注意力增强模块(MRCAEM)利用具有多个不同的卷积核大小的卷积层组合,经过多尺度通道注意力机制可更好地捕捉不同尺度的语义信息,细化多尺度特征的提取。 经特征融合后,通过重建模块可重建出更真实的高分辨率 DEM。 实验证明,该方法的均方根误差(RMSE)对比其他方法降低了约 2% ~ 30% 。
Abstract:
Digital Elevation Models ( DEMs) are considered one of the most important foundational geographic data models, with widespread applications in hydrological analysis,path planning,and modeling. However,the high cost of acquiring large-area,high - resolution DEM data with more precise sensors poses a challenge for many geographic analysis applications. Combining multi - scale features,residual learning, and multi - scale channel attention mechanisms, we propose a digital elevation model super - resolution reconstruction method based on a Multi -Scale Residual Multi -Channel Attention Enhancement Network. The Multi -Scale Residual Multi-Channel Attention Enhancement Module (MRCAEM) utilizes a combination of convolutional layers with multiple different kernel sizes,and through the multi-scale channel attention mechanism,it better captures semantic information at different scales,refines multi-scale feature extraction, and reconstructs more realistic high - resolution DEMs through feature fusion and reconstruction modules.Experimental results show that the proposed method reduces the Root Mean Square Error ( RMSE) by approximately 2% ~ 30% compared to other methods.

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更新日期/Last Update: 2025-03-10