Image Matching Techniques METHODOLOGY

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