Automated DEM generation from SPOT imagery
Zhen Xiong
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Blk. S17 Level 2
Lower Kent Ridge Road, Singapore 119260
Fax: (+65) 67757717,
Email: crsxz@nus.edu.sg
Xiaojing Huang
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Blk. S17 Level 2
Lower Kent Ridge Road, Singapore 119260
Fax: (+65) 67757717
Email: crshxj@nus.edu.sg
Leong Keong Kwoh
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Blk. S17 Level 2
Lower Kent Ridge Road, Singapore 119260
Fax: (+65) 67757717
Email: crsklk@nus.edu.sg
Soo Chin Liew
Centre for Remote Imaging, Sensing and Processing
National University of Singapore
Blk. S17 Level 2
Lower Kent Ridge Road, Singapore 119260
Fax: (+65) 67757717
Email: crslsc@nus.edu.sg
Abstract
This paper presents a new method for fully automated DEM generation from SPOT imagery. It
contains three main steps: Seed points extraction, weighted altitude prediction by multiple seed points, and
image matching. A pair of SPOT images over Hongkong is used to test this method. The whole procedure is
fully automated. No manual editing is made on the DEM generated by the program. The DEM’s accuracy is
checked at the tie points. The mean disparity is 0.53 meters and the standard deviation is 19.2 meters.
Introduction
A Digital Elevation Model (DEM) is one of the most important data set used for geo-spatial analysis.
Unfortunately, DEMs of sufficient point density are still not available for many parts of the earth, and when
available they do not always have sufficient accuracy. Since a DEM enables easy derivation of subsequent
information for various applications, extracting digital elevation models from remote sensing data has
become an important part of international research and development (R&D) programs related to geo-spatial
data [10].
Various methods of generating a digital elevation model (DEM) using remote sensing data have been
investigated. One of the methods is to use two images at a time for the reconstruction of a three dimensional
stereo model in which the altimetric information can be extracted [9]. This method generally contains three
basic steps: setting up sensor mathematical model to reflect the relationship between points on the ground
and pixels on the image, performing image matching to get a disparity map, and finally computing each
point’s altitude.
Stereo image matching is the most difficult step in the process of DEM extraction. For a given pixel on an
image, its corresponding point on the other image is generally searched within a window by image matching.
This window is called the search window. If the size of the search window is too big, the matching procedure.will be very slow and the probability of erroneous matching will also be high. But if the search window is too
small, there is a high probability that the corresponding point is located outside the search window. So it is
very important to choose an optimal size of the search window. A compromise is needed between the two
errors mentioned above. Firstly we should ensure that the corresponding point is located within the search
window, later on we should reduce the size of the search window as much as possible in order to reduce the
probability of incorrect matching.
There are several matching methods that limit the size of the search window. The vertical line locus (VLL)
method [12] adopts a search window that contains all the possible corresponding points. This window
contains the vertical line locus such that the altitude ranges from a minimum value to a maximum value. So it
actually uses a very large search window. Obviously, this matching method is very slow. Due to the large
size of the search window, the probability of incorrect matching is very high. The DEM generated with this
method always contains much noise caused by false matches.
The Brockelbank’s hybrid matching model [1] uses feature-based image matching to first extract some seed
points, and then uses a cubic polynomial to model the horizontal and vertical disparity of every point. If the
seed points distribute normally, the result of this method is good. Otherwise, e.g. in the mountainous area,
the fitting error of the polynomials may be very big. Zhang [11] uses many tie points selected manually to
build up a Delaulay triangulation network, and then interpolates every point’s altitude. He uses this method to
realize three- dimensional reconstructions.
The new method presented in this paper use several criteria to extract seed points, and then use these seed
points to estimate the altitude of the other points surrounding the seed points, and finally image matching is
performed. During the matching procedure, the seed points are extracted simultaneously. This procedure is
iterated until a complete DEM is generated.
