Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Chuanfa Chen"'
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 1, Pp 1568-1588 (2023)
To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergr
Externí odkaz:
https://doaj.org/article/12639b9c756048fe81a9acc9b9cf4820
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103843- (2024)
Satellite global digital elevation models (GDEMs) suffer from positive biases in urban areas due to building artifacts. While various machine learning (ML)-based methods have been proposed to remove these biases, their generalizability is limited by
Externí odkaz:
https://doaj.org/article/8b5d915ad6e74c6aaf129e4f318a6adb
Publikováno v:
IEEE Access, Vol 8, Pp 41000-41012 (2020)
Over the past decades, plenty of filtering algorithms have been presented to distinguish ground and non-ground points from airborne LiDAR point clouds. However, with the existing methods, it is difficult to derive satisfactory filtering results on ru
Externí odkaz:
https://doaj.org/article/7a385aba92bf4b0f910e2504e23af8e1
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 200:89-105
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::259772a78564b95df37ef719426c066e
https://doi.org/10.1016/j.isprsjprs.2023.05.002
https://doi.org/10.1016/j.isprsjprs.2023.05.002
Publikováno v:
IEEE Access, Vol 7, Pp 173166-173184 (2019)
The high noise levels of high-rate Global Navigation Satellite System (GNSS) solutions limit their seismological applications, including capturing earthquake-induced coseismic displacements. In this study, we developed a new adaptive denoising approa
Externí odkaz:
https://doaj.org/article/0fca2fb9e217447ea33d89da5cb72556
Publikováno v:
PLoS ONE, Vol 15, Iss 5, p e0233128 (2020)
[This corrects the article DOI: 10.1371/journal.pone.0176954.].
Externí odkaz:
https://doaj.org/article/f5d8bb80b7f8421183c67dca7bad1909
Publikováno v:
Remote Sensing, Vol 13, Iss 14, p 2663 (2021)
Airborne light detection and ranging (LiDAR) technology has become the mainstream data source in geosciences and environmental sciences. Point cloud filtering is a prerequisite for almost all LiDAR-based applications. However, it is challenging to se
Externí odkaz:
https://doaj.org/article/d88a99540e604b4b875cf2f0e1a2dd0e
Publikováno v:
Remote Sensing, Vol 12, Iss 20, p 3435 (2020)
Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to as
Externí odkaz:
https://doaj.org/article/1995cc56b1e04e8d9dd206cc46fb942e
Publikováno v:
Advances in Space Research. 68:3971-3991
In this study, for feature extraction of seismic- and nonlinear trend terms of the nonlinear and nonstationary high-rate Global Navigation Satellite System (GNSS) seismic displacements, we present an adaptive complete ensemble empirical mode decompos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20c221ee0deac13fd60adedb5f8b0164
https://doi.org/10.1016/j.asr.2021.07.032
https://doi.org/10.1016/j.asr.2021.07.032
A Thin Plate Spline-Based Feature-Preserving Method for Reducing Elevation Points Derived from LiDAR
Publikováno v:
Remote Sensing, Vol 7, Iss 9, Pp 11344-11371 (2015)
Light detection and ranging (LiDAR) technique is currently one of the most important tools for collecting elevation points with a high density in the context of digital elevation model (DEM) construction. However, the high density data always leads t
Externí odkaz:
https://doaj.org/article/f87914ea9d744f35a4d3a332b43a1691