Next Article in Journal
A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index
Next Article in Special Issue
Sliding Windows Method Based on Terrain Self-Similarity for Higher DEM Resolution in Flood Simulating Modeling
Previous Article in Journal
Vicarious Calibration of FengYun-3D MERSI-II at Railroad Valley Playa Site: A Case for Sensors with Large View Angles
Previous Article in Special Issue
Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites
Article

A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1

1
College of Information Engineering, Northwest A&F University, Yangling 712100, China
2
Department of Urbanology and Resource Science, Northwest University, Xi’an 710069, China
*
Author to whom correspondence should be addressed.
Academic Editors: Tomaž Podobnikar and Juha Oksanen
Remote Sens. 2021, 13(7), 1346; https://doi.org/10.3390/rs13071346
Received: 27 February 2021 / Revised: 25 March 2021 / Accepted: 30 March 2021 / Published: 1 April 2021
(This article belongs to the Special Issue Advances in Global Digital Elevation Model Processing)
The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations of mixing errors to effectively remove mixing errors in data from Shuttle Radar Topography Mission 1 (SRTM 1) based on the sparseness of low-rank groups. First, the stripe-error structure that appeared globally in multiple directions was able to be better represented locally using group-sparse regularization and the uniqueness of the data in the low-rank direction of the local range and using variational ideas to constrain the gradient direction of the data to avoid redundant elimination. Second, the nonlocal self-similarity of the weighted kernel norm was used to remove random noise. Finally, the proposed model for eliminating mixed errors was solved using an algorithm based on the multiplier method of alternating direction. Experiments using simulated and real data found that the proposed low-rank group-sparse method (LRGS) eliminated mixed errors in both visual and quantitative evaluations better than the most recent processing methods and existing dataset products. View Full-Text
Keywords: digital elevation model; shuttle radar topography mission 1; low-rank; group sparse; self-similarity; mixed errors digital elevation model; shuttle radar topography mission 1; low-rank; group sparse; self-similarity; mixed errors
Show Figures

Graphical abstract

MDPI and ACS Style

Ge, C.; Wang, M.; Zhang, H.; Chen, H.; Sun, H.; Chang, Y.; Yang, Q. A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1. Remote Sens. 2021, 13, 1346. https://doi.org/10.3390/rs13071346

AMA Style

Ge C, Wang M, Zhang H, Chen H, Sun H, Chang Y, Yang Q. A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1. Remote Sensing. 2021; 13(7):1346. https://doi.org/10.3390/rs13071346

Chicago/Turabian Style

Ge, Chenyu, Mengmeng Wang, Hongming Zhang, Huan Chen, Hongguang Sun, Yi Chang, and Qinke Yang. 2021. "A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1" Remote Sensing 13, no. 7: 1346. https://doi.org/10.3390/rs13071346

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop