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.
Image segmentation is the basis of object-based information extraction from remote sensing imagery. Image
segmentation based on multiple features, multi-scale, and spatial context is one current research focus. The scale
parameters selected in the segmentation severely impact on the average size of segments obtained by multi-scale
segmentation method, such as the Fractal Network Evolution Approach (FNEA) employed in the eCognition software. It
is important for the FNEA method to select an appropriate scale parameter that causes no neither over- nor undersegmentation.
A method for scale parameter selection and segments refinement is proposed in this paper by modifying a
method proposed by Johnson. In a test on two images, the segmentation maps obtained using the proposed method
contain less under-segmentation and over-segmentation than that generated by the Johnson’s method. It was
demonstrated that the proposed method is effective in scale parameter selection and segment refinement for multi-scale
segmentation algorithms, such as the FNEA method.
The situation of geological disaster prevention and control in our country is very serious. The application of remote
sensing technique in geological disaster monitoring has been more than 30 years, and which has got many successful
experiences. Remote sensing technology applications has shown enormous potential in investigation, evaluation, forecast
and warning, and relief aspects of geological hazard research, and has been more and more attention to, for remote
sensing technology is an high technology means with the ability of big range, all-weather, and dynamic monitoring the
spatio-temporal changes of disasters. In the 2008 Wenchuan earthquake disaster, remote sensing technology played a
huge role in the emergency response of the earthquake damage. But it has also exposed some problems that urgent needs
to solve, the most critical problem of which is lacking of fast and efficient methods for disaster information extraction
from remote sensing image data. This problem is also the key technical problems needs to solve and improve in the work
of remote sensing application for geological hazard emergency investigation and evaluation. An interactive interpretation
method platform based on Grab Cut segmentation was proposed in this paper, and used in interaction extraction of
geological hazard information from remote sensing image. Based on ArcGIS Engine second development environment
and in the .net framework, the interactive interpretation method utilize Grab Cut segmentation algorithm was developed.
Because of Grab Cut image segmentation algorithm exploiting the texture and edge features, using this method can
obtain ideal interpretation results and better improve interpreting efficiency, with less interactive steps.
Morphological pattern spectrum was used to shape features description and analysis of visible/near infrared spectra.
Based on the firstly constructed multi-scale Gaussian structure elements, which have similar shape to local feature of
spectra, we calculated pattern spectrums of 10 mineral spectra from USGS mineral spectral library, and then we
compared both the similarity and k-means clustering results of the original spectrum with that of the corresponding
pattern spectrum. The results show that pattern spectrum increases differences between different categories, but retains
similarity within a category, and pattern spectrum is more separable than the original spectra. Mineral spectra
classification has higher accuracy based on pattern spectrum rather than the original spectra.