Development of 2-D and 3-D

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

Automatic detection of edges an meaningful entity has been an area Attempts to find a completely auto for these problems is an active digitization option is used to ma outlines in one image. These manu further refined using the Canny op are found only in the neighbourho edge to ensure better localization. other images using the geome matching procedure. In case the i not able to find corresponding digitization is done in 3-D viewing in Z direction and place each v position in depth during digitiza Positions of cursors in left and Options are available to draw poin shows digitized building boun Washington image. Fig. 2: Digitized buil The process improves edge loca effort of manual identification of ed 2.8 Refinement the Edges Digitization of buildings is the maj fully automatic methods for buildi matured as a result we prefer method. The precision of edges digitized edges to the nearest re manual digitization. This is perform 1. Digitize manually in the proxim 2. Refine the edges that are man densifying and constructing a these points and searching fo image. uring gradients. For the better gradient information from the TM provides the ground height computing the buildingobject Fig. 7 Normalized DSM D Digitization Tool and grouping them to form a rea of research for a long time. tomatic and successful solution ive area of research. A 2-D manually digitize the building anually digitized boundaries are operator. In this case, the edges rhood of the manually digitized . These edges are matched in metrically constrained image e image matching procedure is ing edges in another image, ng mode. User can move cursor vertex of the feature at any tization and fuse the cursors. nd right images are recorded. oints, lines and polygons. Fig. 2 undaries for a portion of uilding boundaries ocalization and minimizes the f edges precisely. ajor step in 3D site modelling, lding extraction are not enough r semi automatic digitization es is attained by refining the real edges which are lost in ormed in the following steps, ximity of the intended building. anually digitized in step-1 by a neighbourhood of each of for real edges in canny edge XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia 299 3. Join the disconnected edges guided by th A simple case of digitizing inner and outer e building a portion of image is shown in t orange boundaries show the manually digiti edge of the building . The upper and lower c figure 3b are the annular region defi threshold; the edges shown in green are obta operator [Canny, 1986]. The boundaries Canny operator provide better accura digitization. Fig. 3: a Digitized boundaries, b Ref

2.9 Matching of Edges and DSM Generat