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

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.