An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network

Abstract

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 NEXTMapV R World 30 TM .

Evaluasi Ketelitian Horizontal Dari Citra Tegak Satelit Resolusi Sangat Tinggi Menggunakan SRTM Dan Terrasar-X Untuk Pemetaan Skala Besar

“…Kedua data DSM tersebut memiliki perbedaan nilai akurasi vertikal. SRTM merupakan salah satu data DSM terbaik yang terbuka untuk umum namun memiliki ketelitian vertikal yang rendah (Wendi et al, 2016). Tujuan dari penelitian ini adalah melakukan evaluasi perbandingan ketelitian horizontal dari orthorektifikasi citra satelit resolusi sangat tinggi menggunakan dua data DSM yang berbeda yaitu SRTM dan Terrasar-X untuk pemetaan skala besar.…”

Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

“…Water 2020, 12, x FOR PEER REVIEW 2 of 14 penetrate vegetation well [9]. The absolute vertical SRTM error was found to be 22.35 m across 255,646 samples in the Amazon rainforest [10], whilst in open areas of South America the equivalent error was at 6.2 m [5].…”

“…Although it is provided at no cost, its accuracy is limited, with a root mean square error (RMSE) of more than 8 m in Singapore's dense urban/forest areas [8]. It was reported that SRTM suffers from inaccuracy especially in areas covered by the canopy, as the 5.6 cm wavelength used does not penetrate vegetation well [9]. The absolute vertical SRTM error was found to be 22.35 m across 255,646 samples in the Amazon rainforest [10], whilst in open areas of South America the equivalent error was at 6.2 m [5].…”

“…There are other types of studies using artificial neural networks (ANNs) for DEM improvement. Wendi et al (2016) presented a promising DEM improvement scheme and showed substantial improvement of SRTM DEM with a RMSE reduction of 52-68% over two different forested areas in Singapore [9]. The author used the ANN together with Landsat 8 multispectral imagery and 92 m resolution of SRTM to eliminate the error caused by dense canopy level in original SRTM.…”

Conservation outputs and recommendations for Nee Soon freshwater swamp forest, Singapore

“…9 Publish work on swamp forest ecology and the development of ecohydrological models in international, peerreviewed scientific journals So far, one guide book (Ho et al, 2016), one book chapter , nine journal papers (Neo et al, , 2017Sun et al, 2015Sun et al, , 2016Chong et al, 2016;Li et al, 2016;Lim et al, 2016;Tan et al, 2016;Wendi et al, 2016) and 18 conference papers have been published. Another eight papers have been accepted for publication Chong et al, 2018;Clews et al, 2018;Ho et al, 2018;Kutty et al, 2018;Lim et al, 2018;Nguyen et al, 2018;Sun et al, 2018).…”