Lampeter case study

The diapositive number 164 of Lampeter was digitised using the Sharp JX-600 scanner at full resolution (600 dpi). In this case the flying height was not given in the photograph, therefore the photograph scale had to be computed by choosing 2 points from a flat area of the map and identifying the same two points in the photograph. The photograph scale obtained was 1:12 000 and the pixel size of the resulting digital image is 0.5m. The area represented in the photograph was covered by two Ordnance Survey 1:2 500 maps used to digitise the field boundaries. The maximum resolution of these maps is 0.5m and they were published in 1979. This data set was processed in the same way as the Isle of Wight data. Figures 7.10 and 7.11 show the different stages of the image processing and the final map and image representation prepared for matching.

Figure 7.8 Isle of Wight map and image polygon and the set of points obtained from the the dynamic programming based matcher

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Chapter 7. Map to image matching A* ' »

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Figure 7.10 The Lampeter original image (top left), the smoothed image (top right), the thresholded image (bottom left) and the edges detected in the image (bottom right)

Chapter 7. Map to image matching

The information related to each of the map polygons and image polygons was extracted by applying the programs code image and code map to the image and map respectively, Appendix C gives the details of the characteristics of each polygon. This information was used to find the matching polygons and Table 7.2 shows the results of the program match code, the values highlighted represent the matched polygons with the respective matching value.

Table 7.2 Lampeter matching values and corresponding matched polygons

M atch in g V alue Map P o ly g o n Im age P o ly g o n 1.258 1 7 0 .5 8 3 2 7 1.132 3 3 1.682 4 22 0.814 5 13 0.979 6 6 1.157 7 1 1.414 8 19 1 .8 5 8 9 9 0.641 10 10 0.976 11 17 1.434 12 6 1.152 13 6 0.899 14 18 1.243 15 19 1.015 16 19 0.898 17 17 1.365 18 5 1.468 19 2 1.845 20 22 0.874 21 17 0.766 22 18 0 .6 2 7 23 1 7 1.079 24 19 1.038 25 10 2.136 26 18 1.147 27 11 1.704 28 1 1.543 29 18 1.352 30 3 1.075 31 11 0.925 32 11 1.010 33 7 1 .2 6 2 3 4 18 0.957 35 10 0.793 36 3 1.342 37 7 1.040 38 6 0.936 39 7

These matched polygons were then further processed in order to obtain the corresponding set of points. The map and image matched polygons were extracted from the original map and image using the information provided by the match_code

program. These polygons were prepared to be matched by the program

map_image_match as described in section 7.3.2. The initial approximation for the transformation between the map and the image was provided by the location of corresponding points selected from the corresponding polygons as described in section 7.3.3.

As in the example described before the method proved to be capable of providing the required map and image list of corresponding coordinates without any relevant problems. The polygons that would manually be matched were accurately detected by the patch matching based on shape technique developed in this study, this information was afterwards used to detect corresponding points between the map and the image, in this way solving the main problem of automatically performing absolute orientation.

7.4 Conclusions

In this chapter the problem of matching a map to an image was investigated. Some techniques suitable to perform this operation were reviewed. Although several automatic methods to find the correspondence between map and image have been suggested, this task is still considered to be a complex topic which is still under research.

The matching system starts by processing both types of data to achieve a common representation suitable for the method to be used. This first stage is crucial since the new representation contains the information to be used by the matching procedure. The new representation of the map and image to be matched can assume various formats, such as quadtree, or structural descriptions. In this particular study polygons are the data representation used, whose information is used by the matching technique applied. The extraction of polygons from the map was a relatively straightforward process, because a particular coverage of a digitised map representing the required polygons was used. The problem of extracting polygons from the image was a more complex procedure. To process the image, several techniques can be used. The method developed consists of carrying out a chain of different operations to enhance the polygons in the image. Intensive research has been taking place in image

Chapter 7. Map to image matching

processing in order to extract relevant information from an image. Although a wide range of tools are already available, this is a topic still under research. In this case the method applied allowed clear identification of some image polygons, however it would be of interest to investigate the subject further. For this study the problem was considered to be an intermediate stage, the solution of which was suitable to carry out the resolution of the problem of automating absolute orientation.

After bringing both map and image to the same line representation of polygons, the matching between the 2 sets of data was carried out, using firstly the patched based matching technique to find correspondent polygons, and secondly the dynamic programming technique to extract conjugate points from those corresponding polygons. The whole procedure is fully described and the various steps are illustrated by two examples. The techniques applied proved to be robust and sufficient to provide the required results, and since each component is amply explained there is scope to improve the system, with the development of other procedures to extract the image polygons, however the main structure should be maintained. The system developed performed well and constitutes a novel approach to automatically determine the relationship between a map and an image. This technique does not require the user to provide an initial approximation to start the process, this is an advantage over many of the attempts previously carried out to automate map to image matching. It was shown that given an image and a map representation of the same area, a chain of procedures can automatically provide a list of map to image correspondent points suitable to be used in absolute orientation.

The following chapter describes how the coordinates of these points are used in order to perform the absolute orientation, which concludes the process of relating the pixel to map coordinates.

Absolute Orientation

8.1 Introduction

Absolute orientation concludes the process that relates image coordinates to ground coordinates. The model which has been created as a result of interior and relative orientation must be scaled, translated and levelled with respect to a ground reference system. The procedure of orienting a stereomodel into an absolute reference system is called absolute orientation. To perform absolute orientation a minimum of two horizontal and three well distributed vertical points are required, but more than the minimum is recommended so that a least squares solution can be achieved (Wolf, 1983). Once the measurements have been taken a three dimensional coordinate transformation is solved between the model coordinate system and the ground coordinate system.

The procedure to perform the transformation between the two sets of three dimensional model and ground coordinates is presented. Its performance is assessed and compared with the corresponding analytical system developed by Kem.

The previous chapter described techniques suitable to automate the absolute orientation. The method developed in this study and described in chapter 7 gives a list of corresponding image and map points. The coordinates of these points are two dimensional, in order to use them to perform absolute orientation the z coordinate has to be attributed to each image and map matched point. The procedure followed to derive height values for features of interest in the map and to obtain the corresponding three dimensional information from the image is described.

Chapter 8. Absolute orientation