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

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

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 erro...

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Bibliographic Details
Main Authors: Chenyu Ge, Mengmeng Wang, Hongming Zhang, Huan Chen, Hongguang Sun, Yi Chang, Qinke Yang
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
digital elevation model
shuttle radar topography mission 1
low-rank
group sparse
self-similarity
mixed errors
Online Access:https://www.mdpi.com/2072-4292/13/7/1346

Internet

https://www.mdpi.com/2072-4292/13/7/1346

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