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18 November 2014 Terrain data conflation using an improved pattern-based multiple-point geostatistical approach
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Abstract
The aim of data conflation is to synergise geospatial information from different sources into a common framework, which can be realised using multivariate geostatistics. Recently, multiple-point geostatistics (MPG) has been proposed for data conflation. Instead of the variogram, MPG borrows structures from the training image, so the spatial correlation is characterised by multiple-point statistics. In pattern-based MPG, two sets of data can be integrated by utilising the secondary data as a locally varying mean (LVM). The training image provides a spatial correlation model and is incorporated to facilitate reproduction of similar local patterns in the predicted image. However, the current patternbased MPG gathers similar patterns based on a prototype class, which extracts spatial structures in an arbitrary way. In this paper, we proposed an improved pattern-based MPG for conflation of digital elevation models (DEMs). In this approach, a new strategy for forming prototype class is applied, which is based on the residual surface, vector ruggedness measure (VRM) and ridge valley class (RVC) of terrain data. The method was tested on the SRTM and GMTED2010 data. SRTM data at the spatial resolution of 3 arc-second was simulated by conflating sparse elevation point data and GMTED2010 data at a coarser spatial resolution of 7.5 arc-second. The proposed MPG method was compared with the traditional pattern-based MPG simulation. Several kriging predictors were applied to provide LVMs for MPG simulation. The result shows that the new method can achieve more precise prediction and retain more spatial details than the benchmarks.
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Yunwei Tang, Jingxiong Zhang, Hui Li, Haifeng Ding, and Linhai Jing "Terrain data conflation using an improved pattern-based multiple-point geostatistical approach", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 926304 (18 November 2014); https://doi.org/10.1117/12.2068647
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