RSPCN
Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks
Bibliographic Data
| ID | 22034635 |
|---|---|
| Authors | Ruichen Zhang (0000-0001-7074-6666, Naval University of Engineering), Shaofeng Bian (0000-0003-1919-2746, Naval University of Engineering, corresponding author), Houpu Li (0000-0001-9698-8911, Naval University of Engineering) |
| Year | 2021 |
| Volume | 10 |
| Issue | 8 |
| Pages | 501 |
| Publication date | 2021-07-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10080501 |
| OpenAlex | W3185807047 |
| Language | EN |
| References cited | 57 |
The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area
Algorithm · Bicubic interpolation · Bilinear interpolation · Computer vision · Convolutional neural network · Digital elevation model · Geography · Multivariate interpolation · Pixel · Preprocessor · Remote sensing · Advanced Image Processing Techniques · Advanced Vision and Imaging · Computer Science · Cryospheric studies and observations · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |