TY  - JOUR
A1  - Wendi, Dadiyorto
A1  - Liong, Shie-Yui
A1  - Sun, Yabin
A1  - Doan, Chi Dung
T1  - An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network
JF  - Journal of advances in modeling earth systems
N2  - 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™.
KW  - SRTM improvements
KW  - Landsat 8
KW  - ANN
Y1  - 2016
U6  - https://doi.org/10.1002/2015MS000536
SN  - 1942-2466
VL  - 8
SP  - 691
EP  - 702
PB  - American Geophysical Union
CY  - Washington
ER  -