深度残差频率自适应的DEM超分辨重建  

Deep Residual Frequency-adaptive DEM Super-resolution

作　　者：李智杰[1] 米德源 李昌华[1] 张颉[1] 董玮 LI Zhi-Jie;MI De-Yuan;LI Chang-Hua;ZHANG Jie;DONG Wei(School of Information and Control Engineering,Xi’an University of Architectural Science and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学信息与控制工程学院,西安710055

出　　处：《计算机系统应用》2024年第12期123-130,共8页Computer Systems & Applications

基　　金：陕西省住房城乡建设科技计划(2020-K09);陕西省教育厅协同创新中心项目(23JY038)。

摘　　要：目前超分辨率重建技术运用于诸多场景,但对于数字高程模型(digital elevation model,DEM)的重建存在许多挑战,针对无法充分利用DEM复杂地形特征导致的细节缺失和失真问题,提出了深度残差频率自适应的DEM超分辨重建模型,由多个高低频特征提取模块组成残差网络结构,提升对DEM特征的整体感知能力,并加入频率选择特征提取模块,增强对复杂地形特征的识别和捕捉能力,其次在模型中加入了空洞空间金字塔池化,通过融合多尺度信息,改善重建质量并充分保留地形特征的细节和结构,最终在梯度域和高度域双重约束下完成超分辨率重建.实验结果表明,在以两种精度的陕西秦岭高程图作为实验数据下,深度残差频率自适应DEM超分辨率模型相较于其他先进模型,在各个指标上均取得了提升,重建后的DEM细节更加丰富、纹理更加清晰.Currently,super-resolution reconstruction technology is applied in various fields.However,digital elevation model(DEM)reconstruction presents numerous challenges.To address the issues of detail loss and distortion caused by inadequate utilization of complex terrain features in DEM,this study proposes a deep residual frequency-adaptive DEM super-resolution reconstruction model.The model consists of multiple high and low-frequency feature extraction modules forming a residual network structure,enhancing the overall perception of DEM features.Additionally,a frequency selection feature extraction module is integrated to improve the identification and capture of complex terrain features.The model also incorporates atrous spatial pyramid pooling,which merges multi-scale information to enhance reconstruction quality and retain detailed terrain features and structures.Final super-resolution reconstruction is completed under dual constraints in the gradient and height domains.Experimental results demonstrate that using elevation maps of the Qinling Mountains in Shaanxi with two different accuracies as test data,the deep residual frequency-adaptive DEM superresolution model outperforms other advanced models across various metrics.Reconstructed DEMs exhibit richer details and clearer textures.

关 键 词：数字高程模型 残差网络 空洞空间金字塔池化 超分辨率重建 

分 类 号：P208[天文地球—地图制图学与地理信息工程] TP18[天文地球—测绘科学与技术] TP391.41[自动化与计算机技术—控制理论与控制工程]