Super-Resolution of Digital Elevation Model with Local Implicit Function Representation

Peng He, Yong Mei Cheng, Ming Dong Qi, Zhi Cao, Heng Zhang, Shao Xian Ma, Shun Yao, Qiang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The Digital Elevation Model (DEM) is an important basic datas for analyzing and exploring the natural environment, hydrology, and geology. Since the cost of improving the DEM accuracy directly from the hardware device is high, it is envisaged to improve the DEM accuracy from the algorithm. Deep learning provides new research ideas for DEM super-resolution. Inspired by the use of implicit neural representation in image super-resolution, this paper combines implicit neural representation with DEM super-resolution, and proposes a DEM super-resolution algorithm based on implicit neural representation, which aims to convert low-resolution DEM (LRDEM) into high-resolution DEM (HRDEM). This paper presents the process of super-resolution of DEM data based on implicit neural representation. The experiment first maps the cropped normalized data to a high-dimensional space through Fourier transform, and then uses the encoder to extract features from the DEM data to generate a feature sequence. The decoder takes the feature sequence and DEM coordinates as input, and then predicts the coordinates of the DEM value. This paper trains and adjusts a super-resolution implicit neural expression model that generates DEM data through a large number of experiments, where the loss function is the norm loss of the predicted value and the true value. The experimental test results are compared with traditional interpolation methods, such as bicubic interpolation, nearest neighbor interpolation and bilinear interpolation. According to the evaluation index data of MSE and PSNR, arbitrary multiple super-resolution based on implicit neural expression can effectively solve the DEM super-resolution problem.

Original languageEnglish
Title of host publicationProceedings - 2022 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages111-116
Number of pages6
ISBN (Electronic)9781665492461
DOIs
StatePublished - 2022
Event2nd International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022 - Guangzhou, China
Duration: 5 Aug 20227 Aug 2022

Publication series

NameProceedings - 2022 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022

Conference

Conference2nd International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022
Country/TerritoryChina
CityGuangzhou
Period5/08/227/08/22

Keywords

  • DEM
  • implicit neural representation
  • positional encoding
  • super-resolution

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