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 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.
Land surface deformation evidently exists in a newly-built high-speed railway in the southeast of China. In this study, we
utilize the Small BAseline Subsets (SBAS)-Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to
detect land surface deformation along the railway. In this work, 40 Cosmo-SkyMed satellite images were selected to
analyze the spatial distribution and velocity of the deformation in study area. 88 pairs of image with high coherence were
firstly chosen with an appropriate threshold. These images were used to deduce the deformation velocity map and the
variation in time series. This result can provide information for orbit correctness and ground control point (GCP)
selection in the following steps. Then, more pairs of image were selected to tighten the constraint in time dimension, and
to improve the final result by decreasing the phase unwrapping error. 171 combinations of SAR pairs were ultimately
selected. Reliable GCPs were re-selected according to the previously derived deformation velocity map. Orbital residuals
error was rectified using these GCPs, and nonlinear deformation components were estimated. Therefore, a more accurate
surface deformation velocity map was produced. Precise geodetic leveling work was implemented in the meantime. We
compared the leveling result with the geocoding SBAS product using the nearest neighbour method. The mean error and
standard deviation of the error were respectively 0.82 mm and 4.17 mm. This result demonstrates the effectiveness of
DInSAR technique for monitoring land surface deformation, which can serve as a reliable decision for supporting highspeed
railway project design, construction, operation and maintenance.
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