The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
2.3 Grid correction
Insito calibration is more and more done to minimise the effects
of the sensor instability. For lower precision photogrammetric
production the insito camera calibration is not mandatory, as the
achieved correction is within sub pixel range. For high accurate
matching with high overlapping images this correction reduces
the noise of the point cloud because remaining image errors
caused by the sensor instability are better compensated [Cramer
2007]. Usually, the benefits of the self calibration are mostly
visible at the model border and comers.
2.4 Ground sampling distance
As the large frame and push broom digital cameras have a fixed
focal length the only way to modify the GSD is to change the
flying height but this also changes the perspective of the images.
Therefore high resolution digital image capture is traditionally
flown at low heights, but here the amount of occluded areas
rises quickly.
Of course a strong overlap of 80% reduces the amount of
excluded areas. Nevertheless, because of the perspective
changes, the image features are less similar than if it were
captured from a higher altitude. This reduces the matching
accuracy and augments the risk of miss matching. In general it
can be said that DSM extraction from high resolution images is
more complicated than DSM extraction from middle resolution
digital imageries. The situation may change with the
introduction of digital cameras with a smaller angle of view.
3. DSM EXTRACTION METHOD
INPHO’s automatic DTM derivation tool MATCH-T DSM has
been redesigned to produce very dense DSM data. The most
important improvement was the introduction of the sequential
multi-image matching and a new robust algorithm for point
filtering.
3.1 Short review of the MATCH-T method
The automatic DTM generation approach in MATCH-T is
mainly characterised by the feature-based matching technique
being hierarchically applied in image pyramids and a robust
surface reconstruction with finite elements.
For DTM extraction the measured 3D points, together with
curvature and torsion constraints are introduced as observations.
The weights for the curvature and torsions observations both
regularize and smooth the DTM.
A complete description of the MATCH-T design can be found
in Krzystek, P. and Ackermann, F., 1995.
3.2 Introduction to the MATCH-T DSM method
The key idea of the MATCH-T DSM method is the automatic
measurement of an extremely large number of irregularly
distributed surface points. Robust statistics can successfully
eliminate gross error to reduce the noise of the point cloud, as
long as most of those points represent the surface and outliers
caused by mismatches or displacement in the scene deviate
from the majority of “good” points in a statistical sense.
3.3 Sequential Multi-Matching
In order to increase the amount of 3D points, the point
extraction is no longer based on static models, but on
computation units. Each computation unit in MATCH-T DSM
chooses the best suited image pairs. Each image pair delivers a
point cloud. The combined point clouds are filtered by a robust
analysis. INPHO calls this extraction method sequential multi
matching.
3.4 From FBM to LSM
The previous MATCH-T versions used feature based matching
for the auto-correlation, where sub-pixel precision is up to one
third of a pixel. In order to improve the matching precision,
LSM can be optionally selected in the new MATCH-T DSM
version. The improvement in height accuracy of the raster is
about 20%, but computation time increases by a factor of two,
thus LSM is optional. The user can decide himself if the 20%
accuracy improvement is worth spending that extra time.
3.5 Model Selection
The selection of the best suited image pairs is based on the
analysis of the DSM slope. The algorithm chooses images that
have the best viewing angle of the matching unit. The algorithm
allows a limitation of the number of models which are used for
the DSM extraction in one matching unit. Indeed with high
overlapping images, the amount of image pair combinations
increases quickly by '/2*(n-l)(n) with n the number of images.
As a significant parameter, the model azimuth direction has
been selected. The point extraction is made in 6 main directions.
If one model delivers not enough 3D points then MATCH-T
DSM selects the next best suited model for this azimuth.
It is possible that some matching units do not have any texture.
For this reason, MATCH-T DSM analyses the quantity of
extracted 3D points and recognizes if the image area has poor or
no texture. Hence, MATCH-T DSM tries up to 20 models
combinations per matching unit.
Figure 1. True 3D filtered MATCH-T DSM
point cloud from aerial images