AUTOMATIC DSM GENERATION FROM LINEAR ARRAY IMAGERY DATA
Zhang Li, Armin Gruen
Institute of Geodesy and Photogrammetry, Swiss Federal Institute of Technology Zurich
ETH-Hoenggerberg; CH-8093 Zurich, Switzerland
Tel.: +41-1-633 31 57, Fax: +41-1-633 11 01
E-mail: <zhangl><agruen>@geod.baug.ethz.ch
Commission III, WG III/2
KEY WORDS: Linear Array Imagery, Image Matching, DSM
ABSTRACT:
CCD linear array sensors are widely used to acquire panchromatic and multispectral imagery for photogrammetric and remote
sensing applications. The processing of this kind of images provides a challenge for algorithmic redesign and this opens the
possibility to reconsider and improve many photogrammetric processing components. In addition, the basic capabilities of image
matching techniques have so far not been fully utilized yet. This paper presents a matching procedure for automatic DSM generation
from linear array imagery data. It can provide dense, precise and reliable results. The method uses a coarse-to-fine hierarchical
solution with an effective combination of several image matching algorithms and automatic quality control. The DSMs are generated
by combination of matching results of feature points, grid points and edges. Finally, a modified multi-photo geometrically
constrained (MPGC) matching algorithm is employed to achieve sub-pixel accuracy for all the matched features with multi-image or
multi-strip image data.
The proposed approach in this paper has been applied to different areas with varying textures and terrain types. The accuracy tests are
based on the comparison between the high quality DEMs / DSMs derived from airborne Laser Scanner or manual measurements and
the automatic extracted DSMs. Results with STARIMAGER, IKONOS and SPOTS HRS images are reported. We demonstrate with
these experiments that our approach leads to good results.
1. INTRODUCTION
In recent years, CCD linear array sensors are widely used to
acquire panchromatic and multispectral imagery in pushbroom
mode for photogrammetric and remote sensing applications.
Linear scanners are carried on aircraft (e.g. ADS40), helicopter
(e.g. STARIMAGER) or spacecraft (e.g. IKONOS) and allow
for photogrammetric mapping at different scales.
Spaceborne optical sensors like SPOT, IKONOS, and QuickBird
provide not only for high-resolution (0.6 — 5.0 m) and multi-
spectral data, but also for the capability of stereo mapping. The
related sensors are all using linear array CCD technology for
image acquisition and are equipped with high quality orbit
position and attitude determination devices like GPS and IMU
systems.
Progress in the development of airborne linear array imaging
system has also been made in the last decade. These systems use
the three-line-scanner concept and provide for high resolution
(0.5 — 0.03 m) panchromatic and multispectral image data with
triplet overlap and along-track base direction. In the year 2000,
Starlabo Corporation, Tokyo designed a new airborne digital
imaging system, the Three-Line-Scanner (TLS) system (now
called STARIMAGER (SI), jointly with the Institute of
Industrial Science, University of Tokyo (Murai, Matsumoto,
2000). The first generation camera STARIMAGER-100 (SI-
100) contains three parallel one-dimensional CCD focal plane
arrays, with 10200 pixels of 7um each. Starlabo is currently
developing a new generation camera system SI-200. This comes
with an improved lens system and with 10 CCD arrays on the
focal plane (3 x 3 work in RGB mode, 1 CCD array works in
infrared mode). Each CCD array consists of 14 404 pixels at
5um size. The system produces seamless high-resolution images
(3 - 10 cm footprint on the ground) with three viewing directions
(forward, nadir and backward). For the SI sensor and imaging
parameters see Gruen, Zhang, 2002.
The processing of this kind of images provides a challenge for
algorithmic redesign and this opens the possibility to reconsider
and improve many photogrammetric processing components,
like image enhancement, multi-channel color processing,
triangulation, orthophoto and DEM generation and object
extraction. We have recently developed a full suite of new
algorithms and software system for the precision processing of
this kind of data.
In this paper, we put particular emphasis on the automatic
generation of DSMs. Originally we developed a matching
approach and the related software “SI-Matcher” for multi-image
processing of the very high-resolution SI images (Gruen, Zhang,
2003). Now this matching procedure has been extended and has
the ability to process other linear array images as well. We will
briefly report about the basic considerations for our procedure.
Then we will address the key algorithms. We will give
experimental results from the processing of SI, IKONOS and
SPOTS HRS images.
2. MATCHING CONSIDERATIONS
Automatic DEM/DSM generation through image matching has
gained much attention in the past years. A wide variety of
approaches have been developed, and automatic DEM
generation packages are in the meanwhile commercially
available on several digital photogrammetric workstations.
Although the algorithms and the matching strategies used may
differ from each other, the accuracy performance and the
problems encountered are very similar in the major systems and
the performance of commercial image matchers does by far not
live up to the standards set by manual measurements (Gruen et
al, 2000). The main problems in DEM/DSM generation are
encountered with
(a) Little or no texture
(b) Distinct object discontinuities
(c) Local object patch is no planar face
(d) Repetitive objects
(e) Occlusions
(f) Moving objects, incl. shadows
(g) Multi-layered and transparent objects
(h) Radiometric artifacts like specular reflections and others
(i) Reduction from DSM to DEM
The degree to which these problems will influence the matching
results is imagescale-dependent. A DSM derived from 5 m
pixelsize SPOTS HRS images or 1 m pixelsize IKONOS images
will be relatively better than one derived from 5 cm pixelsize SI
images. To extract DSMs from very high-resolution aerial
images, we should take into account the occlusions, the surface
discontinuities such as man-made objects and trees, large areas
with little or even no texture, repetitive patterns, etc.
On the other hand, linear array imagery provides for new
characteristics and possibilities for image matching:
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