globalchange  > 影响、适应和脆弱性
DOI: 10.1002/2015MS000536
Scopus记录号: 2-s2.0-84970004073
论文题名:
An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network
作者: Wendi D; , Liong S; -Y; , Sun Y; , doan C; D
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期:2
起始页码: 691
结束页码: 702
语种: 英语
英文关键词: Catchments ; Data flow analysis ; Forestry ; Low pass filters ; Mean square error ; Neural networks ; Pattern recognition ; Remote sensing ; Stereo image processing ; Tracking radar ; Digital elevation model ; Innovative approaches ; LANDSAT ; Multi-spectral imagery ; Publicly accessible ; Root mean square errors ; Shuttle radar topography mission ; SRTM improvements ; Surveying
英文摘要: Although the Shuttle Radar Topography Mission [SRTM) data are a publicly accessible Digital Elevation Model [DEM) provided at no cost, its accuracy especially at forested area is known to be limited with root mean square error (RMSE) of approx. 14 m in Singapore's forested area. Such inaccuracy is attributed to the 5.6 cm wavelength used by SRTM that does not penetrate vegetation well. This paper considers forested areas of central catchment of Singapore as a proof of concept of an approach to improve the SRTM data set. The approach makes full use of (1) the introduction of multispectral imagery (Landsat 8), of 30 m resolution, into SRTM data; (2) the Artificial Neural Network (ANN) to flex its known strengths in pattern recognition and; (3) a reference DEM of high accuracy (1 m) derived through the integration of stereo imaging of worldview-1 and extensive ground survey points. The study shows a series of significant improvements of the SRTM when assessed with the reference DEM of 2 different areas, with RMSE reduction of ∼68% (from 13.9 m to 4.4 m) and ∼52% (from 14.2 m to 6.7 m). In addition, the assessment of the resulting DEM also includes comparisons with simple denoising methodology (Low Pass Filter) and commercially available product called NEXTMap® World 30™. © 2016. The Authors.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75915
Appears in Collections:影响、适应和脆弱性
气候变化与战略

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作者单位: Tropical Marine Science Institute, National University of Singapore, Singapore; Willis Research Network, Willis Re Inc., London, United Kingdom; Center for Environmental Modeling and Sensing, SMART, Singapore; University of Potsdam, Institute of Earth and Environmental Science, Germany; GFZ German Research Centre for Geosciences, Section 5.4 Hydrology, Germany

Recommended Citation:
Wendi D,, Liong S,-Y,et al. An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(2)
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