The spacecraft has three modes of image modes are termed as spot, paintbrush and mu
spot mode covers a swath of 9.6 km with s from 6 to 290 km. The paintbrush mode
combined swath by imaging adjacent strips multiview mode, the same area is imaged
different view angles from the same orbit This mode is useful for computation of he
However, due to continuous variation of pi track resolution and the base to height ratio
acquisition varies for each imaged line. D modelling the imaging geometry of s
complex. A physical sensor model is develo account the dynamic nature of imaging proce
Fig. 1: Cartosat-2 Multiview Imag
2.2 Physical Sensor Model for Cartosat-2
The Cartosat-2 spacecraft is equipped with system, star sensors and gyros to provid
orientation information at regular time inte sensor model utilizes this information in
coherent manner. The model does not appro the orbit. The osculating nature of the orb
converting the position and velocity par varying Keplerian elements, which are interp
position at the time of imaging. The orienta available as set of quaternions, which are
angles. The residual orientation error is mod pitch, and yaw over a short segment of
precise control points are not available the is improved using control points identifi
Thematic Mapper ETM orthoimages and S The model is based on well known collinear
states that the object position, image perspective centre lie on a straight line at the
Equation 1 represents the collinear mathematical form.
. . In equation 1, x and y represent the image
is the effective focal length of the imaging co-ordinates of the object point and X
p
, Y
p
, of the perspective centre at the time of imag
denoted by s. M is the transformation ma image and the object space. The matrix
multiplying a series of rotations connectin ordinate systems.
2.3
Relative Orientation of Multiview Im
The residual orientation error in the multivi acquisition is highly correlated for the ove
age acquisition; these multiview modes. The
th strip length varying ode provides a wider
ps from same orbit. In ed from two or three
it as shown in Fig. 1. height of the objects.
pitch rate, the along- io of multiview image
Due to these factors spacecraft becomes
eloped that takes into ocess of Cartosat-2.
aging Mode
2
th satellite positioning ide the position and
tervals. The physical in a systematic and
roximate the shape of orbit is accounted by
parameters to slowly terpolated to know the
ntation information is re converted to Euler
odelled as bias in roll, f imaging. In case,
he positional accuracy tified from Enhanced
SRTM DEM. earity condition which
e position and the the time of imaging.
earity condition in 1
ge plane coordinates, f ng system, X, Y, Z are
, Z
p
are co-ordinates aging. Scale factor is
matrix connecting the rix M is formed by
cting intermediate co-
Images
tiview mode of image verlapping images as
these images are acquired within a the same orbit. Thus, it is possible
orientation of these images by orientation error as unknown. This
advantages; first, it is easy to identi overlapping images and secondly
points are not available, relatively for computation of relative heights
The developed approach to rela images is based on coplanarity
perspective centres and the objec plane. Mathematically the coplanar
, , where
, , represents scalar triple vectors joining object point and t
first and second images respectiv connecting two perspective centre
with respect to differential roll, p pairs of conjugate points are suffi
correction. The position of the orientation
information is
p onboardground processed measu
images. 2.4
Computation of Rational Po
Over the past decade, rational func alternate to physical sensor models
fact that physical sensor model information about the camera geom
of image acquisition process. O function models are easy to implem
commercial satellite imagery p rational function model in place of
the system truly sensor independen quantify the results acquired with p
rational function model. Rational polynomial coefficients
independent approach Tao, 2002 computes the orientation param
explained in previous section. The obtained for the given object s
linearized form of equation 1 M image positions for a given set o
object space co-ordinates are es sensor model. The set of object poi
positions are used to compute coefficients. The derived set of rat
are used for relating image and ob polynomial coefficients are used
possible to use commercially avai from satellites such as Geoeye-1, W
for site model generation.
2.5 Image Matching Techniques
Digital image matching technique digital surface model and automat
points. Digital image matching is c ill posed problem. This problem
posed one by imposing regulariz technique is to reduce the dom
introducing geometric constraints problem line segments are automat
using geometric and photometric c in a short interval of time from
ible to perform analytic relative y considering the differential
his approach has two significant ntify the conjugate points in the
dly, if precise ground control ly oriented images can be used
ts of the objects. elatively orient the multiview
y condition. It states that the ject point lie on the epipolar
narity condition is expressed as
0 3
ple product and ,
are the d the perspective centre of the
tively, represents the vector tres. Equation 3 is linearized
l, pitch and yaw values. Three fficient to compute differential
e perspective centres and the primarily
obtained from
asurements supplied with the
Polynomial Coefficients
nction models are being used as els. This is primarily due to the
dels are complex; they need ometry and good understanding
On the other hand, rational lement and supported by major
providers. Moreover, using of physical sensor model makes
ent. However, it is important to h physical sensor model and the
ts are computed using terrain 2. The physical sensor model
rameters as per the method The image space co-ordinate is
t space co-ordinate using the Mahapatra et al, 2004. The
t of uniformly spaced grid of estimated using the physical
points and corresponding image ute the rational polynomial
rational polynomial coefficients object space. Since the rational
ed for further processing, it is vailable stereo images obtained
, Worldview-12, and IKONOS
ues
ues are used for extraction of atic identification of conjugate
is considered as mathematically em can be transformed to well
rizing constraint. One possible omain of probable match by
s. In building reconstruction atically extracted and matched
ic constraints Baillard C ,
Park,
XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia
298
S, Schmid,C.. Some researchers propos information for edge extraction and lin
matching[Ok, A.O, Scholze, S.], our approa to extract and match line segments for two r
rooftops are modelled which means that sam to multiple points, thus, it is sufficient to m
edge of the building to compute height o Building boundaries are digitized, refined
nadir image. These boundaries along information analytically transferred to the
relation between geometrically uncorrecte image and ortho image is established using th
If two overlapping images are relatively disparity between two conjugate points is du
relief. Based on the geometry either the matched in original stereo pair or the other i
to form set of epi-polar images. In case of C angle about roll axis is significant; this mean
due to terrain variation will not be restricted Image matching strategy has to take into
acquisition process. The disparity map resolution is utilized to guide the matching i
cases geometric constraint is used to limit th dense DSM is generated using area based m
performed on epi-polar images, while featu matches the edges of buildings in raw as w
images using the geometric constraints. F matching techniques, normalized cross corr
similarity measure. In both the matching a size is increased dynamically. The sim
normalized cross correlation initiates with a two thresholds, termed as noise and acceptan
normalized cross correlation coefficient for t than or equal to the acceptance correlation
is checked for forward and reverse matching match point if the correlation coefficient
acceptance threshold for both the direction coefficient is less than the acceptance thresh
the noise threshold, the template size for re space is increased and correlation coeffici
The noise threshold is selected as 0.4 and a is selected as 0.9. Initial window size of the
by 13 which is increased in steps of two pix repeated for at least three different sized win
is repeated for each level of image pyram points are transferred to the next level using
interpolation for unmatched point is not do original resolution images.
2.6
Computation of Normalized DSM
A Digital Terrain Model DTM is the elev landscape which does not include above gro
other hand, a Digital Surface Model DSM with their heights above the ground as well
The man-made objects with different heigh can be detected by applying a threshold
Model. The DTM is estimated using mathem The morphological operators help in bring
terrain from the DSM. The above ground o using the DSM and morph output. T
operators, namely “opening” and “closing” The size of the window depends upon the
The normalized DSM is generated by su from the DSM. Segmentation and area
buildings of desired size. Finally the build posed use of colour
line segment stereo roach does not attempt
o reasons 1 only flat ame height is assigned
o match points on the t of the building. 2
ed and connected on g with the height
e ortho image as the ed image, epi-polar
g the sensor model. ly oriented then the
due to the topographic the set of points are
er image is re-sampled f Cartosat-2, often the
eans that the disparity ted to one dimension.
to account the image p obtained in lower
g in next step; In both t the search space. The
d matching techniques ature based matching
s well as in epi-polar . For both the image
orrelation is used as a g approachs, template
imilarity measure of a small template and
tance threshold. If the r the template is more
n threshold, the point ing. It is accepted as a
ent is more than the ons. If the correlation
eshold but higher than r reference and search
ficient is recomputed. d acceptance threshold
the search image is 13 pixels. The process is
windows. This process amid .The unmatched
ing interpolation. The done for matching of
levation model of the ground objects. On the
includes the objects ell as the topography.
ights over the terrain to Digital Surface
ematical morphology. nging the background
d objects are detected Two morphological
” are used iteratively. e size of the building.
subtracting the DTM ea filters detect the
ilding outline can be constructed using the neighbourin
definition of the building, the gr gray image is also used. The DTM
which is used as an input for co height.
Fig. 6 Derived DSM Fig
2.7 Development of 2-D and 3-D
