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THEORY AND ALGORITHMS OF DSM GENERATION FROM MULTI-LINE-ARRAY
IMAGES MATCHING
LeiRong*, Fan Dazhao, Ji Song, Zhai Huiqin
Zhengzhou Institute of Surveying and Mapping, Zhengzhou, 450052, China - leirong@163.com
Commission VI, WG VI/5
KEY WORDS: Multi-line-array digital images, DSM; CCD; ADS40; Correlative Coefficient; POS
ABSTRACT:
A new multiple images matching theory model is proposed in this paper to generate DSM from aerial three line array digital images.
Through this model, multiple images (more than 2) can be matched simultaneously and the epipolar line constraint can be used
indirectly. Theoretically, the occlusion and multiple solution problems, which are unavoidable in traditionally image matching
process, can be greatly solved by this model. Based on the robust multi-image matching algorithms, intelligent DSM generation
procedure is constructed, and some key techniques during the DSM generation process are investigated. Experiments prove that the
DSM generation methods proposed in this paper can effectively generate reliable DSM form multi-line-array digital images.. At the
end of this paper, problems remaining to be studied are presented.
1. INTRODUCTION
With the development of sensors and their application
technique since 1990s, it will not take a long time for film
cameras to be replaced by CCD digital cameras., which is one
of the most significant developing trends of aerial
photogrammetry. Now there are two developing trends for CCD
digital camera, one is big plane array and another is linear array.
Currently, plane array CCD doesn’t have enough pixels to meet
the requirements of practical photogrammetric applications. To
solve this problem, one way is combining a few plane array
CCD to produce a big one, but it is expensive and brings the
other problems in the real-time transferring and storing of
massive changing data. Additionally, some arrays of plane array
CCD are randomly distributed, which will result in the losing of
image pixels and the bringing of more parameters for geometric
and radiometric correction than linear array CCD. Thereby,
under the existing research condition, linear array CCD digital
cameras are the optimal choice for aerial photogrammetry. At
present, some leading experimental and commercial digital
camera such as WAOSS, MOMS, WAAC, DPA, ADS40, TLS
and JAS and so on are all three line array CCD digital cameras.
Among these digital camera systems, ADS40 is the first
commercial airborne three line array digital camera system
developed by Leica Company and the DLR institution.
Digital Surface Model (DSM) is often referred to as the model
for the first reflective or visible surface. DSM is an vital
product of digital photogrammetry and plays an irreplaceable
role in such aspects as determining objects’ height, generating
DEM, producing true ortho-image, automatic recognizing and
extracting buildings and so on. Recently, the techniques of
digital sensor have undergone a significant development and
many sensor systems can obtain multiple images of the same
area simultaneously. As an example, for ADS40, there are 3 to
7 highly overlapping images on the same flight strip (the
overlapping degree of neighboring images is nearly 90%) for
any imaging area. Moreover, the ADS40’s overlapping degree
of neighboring flight strip is around 60% and this provides
more images for the imaging area. The high overlapping images
provides redundant information for automatic DSM generation.
However, it is quite complex to generate DSM form three line
array digital images and multiple image matching technique is
one bottleneck. To generate dense and precise DSM, such
problems as image occlusion, multiple solution, noise, surface
discontinuity and so on have to be solved effectively, which
requires new image processing techniques.
2. MULTI-IMAGE MATCHING ALGORITHM MODEL
As is shown in Figure 1, consider three ADS40 digital images,
which are obtained on the same flight line. 10, II and 12 are
nadir, forward and backward image respectively, where 10 is
selected as reference image, and Ii (i=l, 2) are selected as
searching images.The basic working process of AMMGC
model can be described as follows.
(1) Define or extract points pi(i=0, 1, 2, 3, ...) to be matched on
the reference image.
(2) For each point pi, determine its approximate height Zi and
height error AZi. Zi and AZi can be predefined by users, or
obtained by rough matching of high layer image pyramids or
initial DSM. The quasi-epipolar lines of pi on the searching
images are determined by known exterior parameters and line
fitting methods. Define an image window W around pi, and W
is named as correlation window.
(3) On one searching image’s quasi-epipolar line, select a
searching window (the same size as the correlation window)
around each point of the line, compute the correlation
coefficient between the searching window and correlation
window, and find the local max correlation coefficients.
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