标題：Fusion of multi-scale DEMs using a regularized super-resolution method作者：Linwei Yue, Huanfeng Shen, Qiangqiang Yuan, Liangpei Zhang

來源出版物：International Journal of Geographical Information Science 卷：29 期：12頁：2095-2120 DOI：10.1080/13658816.2015.1063639 出版年：

摘要：The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted L1 norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.

文獻類型：Article

語種：English

作者關鍵詞：multi-scale DEMs, data fusion, regularized framework, super-resolution

通訊作者地址：Huanfeng Shen; School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei province, China

電子郵件地址：shenhf@whu.edu.cn

影響因子（2014）：1.655