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Automating the process of digital map generation
Photogrammetria, 40 (1985) 155--163 Elsevier Science Publishing B.V., Amsterdam -- Printed in The Netherlands
155
A U T O M A T I N G T H E P R O C E S S O F D I G I T A L MAP G E N E R A T I O N
R. LUBKOWITZ and W.-D. GROCH
Forschungsinstitut f~r Informationsverarbeitung und Mustererkennung (FIM-FGAN) (Research Institute for Information Processing and Pattern Recognition), Eisenstockstr. 12, D7505 Ettlingen 6, Germany. (Received March 29, 1984)
ABSTRACT Lubkowitz, R. and Groch, W.-D., 1985. Automating the process of digital map generation. Photogrammetria, 40 : 155--163. An essential step towards the automatic generation of digital maps is the automatic extraction of cartographic features such as roads, highways, railways, rivers, waterways, forest areas, etc. from maps. Several methods have been developed to solve this task. By the analysis of the map legend, the actual parameter values describing the relevant cartographic features can be found automatically. The extraction procedures have been implemented and tested successfully by application to different map master folios. The extraction process has been supported and improved substantially by a simultaneous processing of the map and an aerial image of the same area.
INTRODUCTION I n t h e f u t u r e , c a r t o g r a p h i c i n f o r m a t i o n will have t o be available in digital f o r m o n a c o m p u t e r n o t o n l y f o r c a r t o g r a p h i c d a t a bases and i n f o r m a t i o n systems, b u t also b e c a u s e t h e u p d a t e o f m a p s is simplified and speeded up t o a large e x t e n t , the p r o d u c t i o n o f special issue m a p s will b e c o m e m o r e flexible a n d a u t o m a t i c s o l u t i o n s f o r generalization are usable o n l y u n d e r this condition. F u r t h e r m o r e w i t h this premise t h e a p p l i c a t i o n o f c a r t o g r a p h i c inform a t i o n in a u t o m a t i c navigation and r e m o t e - s e n s i n g s y s t e m s can be achieved. Usually t h e t r a n s f o r m a t i o n o f c a r t o g r a p h i c i n f o r m a t i o n into digital f o r m is p e r f o r m e d m a n u a l l y b y m e a n s o f a digitizing table, This is very t i m e - c o n suming a n d e r r o r b o u n d . O n l y f o r s o m e special tasks d o s e m i - a u t o m a t i c solut i o n s exist (e.g. t h e d i g i t i z a t i o n o f c o n t o u r lines). A n interactive p r o c e d u r e for t h e e x t r a c t i o n o f c a r t o g r a p h i c signatures f r o m m a p s is p r e s e n t e d . A f t e r starting the process t h e i n t e r p r e t e r supervises the p e r f o r m a n c e a n d c o m p l e m e n t s and c o r r e c t s t h e results if necessary.
0031-8663/85/$03.30
© 1985 Elsevier Science Publishers B.V.
156 DATA
PREPARATION
For test purposes parts of the topographic map 1 : 50.000 (TK50) were used. Other topographic maps have similar signatures, so that the developed methods are not restricted to this special kind of map. In Fig. l a an example of a part of a military version of the TK50 is shown covering an area of 5 km by 5 km on the ground. Due to the high density of information and the complexity of several signatures, an extraction from the c o m p o u n d map is not very successful. Therefore, the map master folios made for the color print are used for the extraction. If these folios are not available, similar folios can be produced artificially by scanning the c o m p o u n d map with red, green and blue light. A suitable operation on the color scans yields the so-called color folios, which contain separately -- in analogy to the master folios -- the red signatures {e.g. road fillings and contour lines), blue signatures (e.g. waterways), green signatures (e.g. forest areas) or black signatures (e.g. railways). Figures l b and lc show an example of a red and black master folio, Fig. l d - - f show the red, blue and green color folios of Fig. la.
a)
'b)
Fig. 1. Examples of test material.
157 EXTRACTION OF LINE SIGNATURES The main topic of the investigation was the line signatures. A special method for area-shaped signatures was also developed but is not presented here. The methods for the extraction of line objects were originally developed to extract line objects from aerial images [1, 2]. While retaining the principles, details of the extraction methods had to be adapted to the specific features of cartographic signatures. The folios can be regarded as quasi-binary images with constant width and gray level of the signatures. But some line signatures have a complicated design, because they are assembled from different parts (e.g. the railway signature). Other signatures (e.g. the signatures of secondary roads in the military edition of the TK50) show gaps at regular intervals. Two extraction methods developed (local and regional method) are used for different types of line signatures. The main characteristic of the methods is their sequential and object guided manner. The input data are not processed systematically and exhaustively, but the calculations are concentrated on promising parts of the scanned folios. This is achieved by following the signatures. The extraction methods have to distinguish between the different types to avoid the change over from one signature to another. For the start of the extraction, starting points have to be chosen either interactively or by an automatic search, which must ensure that starting points are detected on the signatures of interest only. By the analysis of the map legend the parameter values describing the relevant cartographic features can be derived automatically. For the extraction of roads, the red master or color folio is used which contains the signatures of the roads. While the highways and the main roads are drawn as full lines with different width, the secondary roads in the military edition of the TK50 are represented by dashed lines with gaps at regular intervals of nearly the same length as the drawn line segments. Figures 2a--f show the extraction of the main and secondary roads of Fig. lb. First starting points for the main and secondary roads are searched for automatically along several sample lines (image lines or columns), which are indicated by the white marks at the left border (Fig. 2a and b). Starting from the top starting point of the main roads, the local extraction m e t h o d follows the signature step by step [5, 6]. In every step a sample line shaped like an arc is defined and evaluated to detect the next object point. Figure 2c shows an example for one step of the local method. At the left border the gray level diagram along the sample line is displayed. One can clearly recognize a short interval with low gray levels, indicating the profile of the cross-section with the line signature. In Fig. 2d all main roads are extracted by using only the first starting point. During the extraction two intersections with secondary roads are recognized and marked. Starting from these intersections, the secondary roads are extracted in a second processing step (Fig. 2e). For the extraction of the t w o secondary roads in the upper half of the image, the respective starting points shown in Fig. 2b were used (see Fig. 2f).
158
The signature of the railways is contained in the black master folio (see Fig. lc). The extraction of the railway is difficult due to t w o reasons: The signature of the railway is not drawn as full line and the other numerous signatures contained in the black master folio disturb the extraction. Embankm e n t signatures are n o t separated from the railway signature and falsify the profile of the object cross-section, signatures of stations interrupt the line, in the neighborhood of stations and switches the filling of rectangles is often
a>
b)
c)
d
e)
f)
Fig. 2. Extraction of main and secondary roads.
a)
b)
Fig. 3. Extraction of the railway signature.
c)
159 missing, etc. Due to the complicated design, two different degrees of image resolution are used for the starting point search. At first candidates are searched for in the coarser resolution, then these are examined in a finer resolution to decide on acceptance or rejection. Figure 3a shows the starting points f o u n d for the railway of Fig. lc. The regional extraction m e t h o d [5, 6] is used for the extraction of the railway. It follows the signature step by step. In every step a rectangular sample area is defined and evaluated to detect a complete segment of the line (consisting of a black and a white signature element; see Fig. 3b). Figure 3c shows the final result of the railway signature extraction. The signatures of the waterways are extracted by the local extraction m e t h o d from the blue master or color folios. COOPERATIVE EXTRACTION OF LINE SIGNATURES The simultaneous use of maps and aerial images of the same area results in advantages and new application possibilities for the extraction methods [4, 5, 6]. The processing of several images with different geometries requires an image registration. In the procedure presented, a projective transformation, which needs four pairs of control points, is used. If the area imaged is not planar, we get deviations from the correct locations, which are taken into account by the extraction methods. For the cooperative extraction two extraction modes are available: the supporting and the verifying mode. The extraction is performed in the first image as long as new results can be found, then the results are mapped into the other image. In the case of the supporting mode, the results are accepted as final results for both images. To continue the extraction, the dead ends (end segments of the object parts found) are centered (shifted to the correct location) if necessary. The extraction continues at the centered dead ends. It stops, if an object part of a predefined length has been extracted. The results are mapped back into the first image, the extraction is continued, etc. If the verifying mode is chosen, the results mapped to the other image are taken as preliminary results for both images. The verification process tries to find the respective object parts by means of a modified regional extraction method. Therefore, chains of sample areas are positioned along the mapped results. Only objects, which can be found in both images, are accepted as final results. Objects, which can be found only in one image, are presented to the human interpreter as possible changes. Starting from the dead ends, which have been produced by the verification process, the extraction process can be continued. Thus extraction and verification are performed alternately in both images, until no continuation can be found in either image. The following figures show examples of cooperative extractions from two images, the red color folio of Fig. l d and an aerial image of the same area (Fig. 4c). The main and secondary roads are extracted from the folio and the aerial image either supports the extraction (Fig. 4a--f) or is used for verification (Fig. 5a--f). In Fig. 6a--f the results are displayed when starting the supporting cooperation in the aerial image.
160 Figure 4a shows the starting points which have been found for the extraction of the mare roads. In Fig. 4b the result o f the extraction of the main and secondary roads is presented, where only one o f the starting points was used. The extraction stops at two locations on the secondary roads, because several c o n t o u r lines, which lie close together, or a sharp bend in a gap of the signature, make an unambiguous continuation impossible. Figure 4d shows the extraction result mapped into the aerial image. In Fig. 4e the extraction has been continued after the centering of the dead ends until road segments of an interactively defined length have been extracted. The result is mapped back into the folio, the dead ends are centered and the extraction is continued up to the border (see Fig. 4f).
a)
e)
f)
Fig. 4. Example of a cooperation : the extraction from a color folio is supported by aerial image information. While in the first cooperation example the aerial image was only used to support the extraction, it is used in Fig. 5a--f for verification, too, In this way locations are detected at which the cartographic information does not (any longer) match the information o f the aerial image. As in the first ex. ample the extraction starts in the red folio and the result is mapped into the aerial image for verification (Fig~ 5a). In Fig, 5b an intermediate result with a sample area is displayed, in Fig, 5c the final result of the verification is presented. To get more results the extraction is continued in the aerial image,
161
see Fig. 5d. The new extraction result is mapped back into the folio (Fig. 5e) and verified (Fig. 5f). This is the final result of the cooperative extraction, because continuations, extracted from the folio, cannot be accepted for the aerial image. The last figure shows an example for the support of the extraction from an aerial image b y a map. In Fig. 6a the result of the extraction from the aerial image is displayed. The result is mapped into the folio (Fig. 6b) and the dead ends are centered. Then the extraction is continued for a predefined distance (Fig. 6c). The two lines, which have been found on walkways in the aerial image on the right-hand side, have the same appearance as the roads, b u t they are not contained in the red color folio and therefore n o t continued. Figure 6d shows the result mapped back into the aerial image. Figure 6e and f represent the final result of the cooperation for the aerial image and the folio.
a)
b)
c)
d)
e)
f)
Fig. 5. E x a m p l e of a c o o p e r a t i o n : the e x t r a c t i o n results from folio and aerial image are alternately verified.
CONCLUSIONS The process of digital map generation can be significantlyspeeded up by the automatic extraction of cartographic signatures. Methods are described
162
a)
b)
d)
e
c)
Fig. 6. Example of a cooperation: the extraction from an aerial image is supported by cartographic information.
which are useful tools for the interpreter-guided extraction of line signatures. An aerial photograph o f t h e same area as the map is used t o improve the performance o f the methods. Interactions are reduced to a minimum to supervise the process and assess the results. Usually the results are complete and reliable some minor additions m a y sometimes be necessary, but in most cases, no corrections are required. The m e t h o d s developed are n o t restricted to the digital map generation but can also be used for the evaluation o f multlsensor image data. This includes t h e evaluation o f image pyramids {e.g. for the extraction of objects of largely different width), multitemporal data {e.g. for change detection) and stereo images {e.g. for parallax calculation along line objects). ACKNOWLEDGEMENTS The authors wish to thank P. Fritsche, H. Mayer, R. Neu, P. Schlippe, and Dr. M. Sties, who were involved in this investigation.
163 REFERENCES 1 Groch, W.-D., 1982. Extraction of line shaped objects from aerial images using a special operator to analyze the profiles of functions. Computer Graphics and Image Processing, Vol. 18. Academic Press, New York. 2 Bausch, U., Groch, W.-D., Kestner, W. and Sties, M., 1980. Teilautomatische Objektextraktion aus Luftbildern und Landkarten. Nachr. Karten- Vermess., Reihe I: Ori~inalbeitr~ge, 81: 7--21. 3 Groch, W.-D., 1982. Automatisierung bei der Digitalisierung grossmaszst//blicher Karten. Nachr. Karten- Vermess., Reihe I: Originalbeitr~ge, 89: 41--48. 4 Lubkowitz, R., 1983. Kooperative Linienextraktion aus Luftbild und Karte. Nachr. Karten- Vermess., Reihe I : Originalbeitr~ge, 92 : 73 --82. 5 Groch, W.-D., Lubkowitz, R., Schlippe, P. and Sties, M., 1982. Automatisierung der Extraktion kartographischer Signaturen aus Landkarten mit Unterstiitzung durch Luftbilder. Teil 1 : Uberblick iiber die entwickelten Verfahren und Ergebnisse. FIM-Bericht No. 104, Karlsruhe. 6 Groch, W.-D., Lubkowitz, R., Schlippe, P. and Sties, M., 1982. Automatisierung der Extraktion kartographischer Signaturen aus Landkarten mit Unterstl]tzung durch Luftbilder, Teil 2: Technischer Anhang. FIM-Bericht No. 106, Karlsruhe.
155
A U T O M A T I N G T H E P R O C E S S O F D I G I T A L MAP G E N E R A T I O N
R. LUBKOWITZ and W.-D. GROCH
Forschungsinstitut f~r Informationsverarbeitung und Mustererkennung (FIM-FGAN) (Research Institute for Information Processing and Pattern Recognition), Eisenstockstr. 12, D7505 Ettlingen 6, Germany. (Received March 29, 1984)
ABSTRACT Lubkowitz, R. and Groch, W.-D., 1985. Automating the process of digital map generation. Photogrammetria, 40 : 155--163. An essential step towards the automatic generation of digital maps is the automatic extraction of cartographic features such as roads, highways, railways, rivers, waterways, forest areas, etc. from maps. Several methods have been developed to solve this task. By the analysis of the map legend, the actual parameter values describing the relevant cartographic features can be found automatically. The extraction procedures have been implemented and tested successfully by application to different map master folios. The extraction process has been supported and improved substantially by a simultaneous processing of the map and an aerial image of the same area.
INTRODUCTION I n t h e f u t u r e , c a r t o g r a p h i c i n f o r m a t i o n will have t o be available in digital f o r m o n a c o m p u t e r n o t o n l y f o r c a r t o g r a p h i c d a t a bases and i n f o r m a t i o n systems, b u t also b e c a u s e t h e u p d a t e o f m a p s is simplified and speeded up t o a large e x t e n t , the p r o d u c t i o n o f special issue m a p s will b e c o m e m o r e flexible a n d a u t o m a t i c s o l u t i o n s f o r generalization are usable o n l y u n d e r this condition. F u r t h e r m o r e w i t h this premise t h e a p p l i c a t i o n o f c a r t o g r a p h i c inform a t i o n in a u t o m a t i c navigation and r e m o t e - s e n s i n g s y s t e m s can be achieved. Usually t h e t r a n s f o r m a t i o n o f c a r t o g r a p h i c i n f o r m a t i o n into digital f o r m is p e r f o r m e d m a n u a l l y b y m e a n s o f a digitizing table, This is very t i m e - c o n suming a n d e r r o r b o u n d . O n l y f o r s o m e special tasks d o s e m i - a u t o m a t i c solut i o n s exist (e.g. t h e d i g i t i z a t i o n o f c o n t o u r lines). A n interactive p r o c e d u r e for t h e e x t r a c t i o n o f c a r t o g r a p h i c signatures f r o m m a p s is p r e s e n t e d . A f t e r starting the process t h e i n t e r p r e t e r supervises the p e r f o r m a n c e a n d c o m p l e m e n t s and c o r r e c t s t h e results if necessary.
0031-8663/85/$03.30
© 1985 Elsevier Science Publishers B.V.
156 DATA
PREPARATION
For test purposes parts of the topographic map 1 : 50.000 (TK50) were used. Other topographic maps have similar signatures, so that the developed methods are not restricted to this special kind of map. In Fig. l a an example of a part of a military version of the TK50 is shown covering an area of 5 km by 5 km on the ground. Due to the high density of information and the complexity of several signatures, an extraction from the c o m p o u n d map is not very successful. Therefore, the map master folios made for the color print are used for the extraction. If these folios are not available, similar folios can be produced artificially by scanning the c o m p o u n d map with red, green and blue light. A suitable operation on the color scans yields the so-called color folios, which contain separately -- in analogy to the master folios -- the red signatures {e.g. road fillings and contour lines), blue signatures (e.g. waterways), green signatures (e.g. forest areas) or black signatures (e.g. railways). Figures l b and lc show an example of a red and black master folio, Fig. l d - - f show the red, blue and green color folios of Fig. la.
a)
'b)
Fig. 1. Examples of test material.
157 EXTRACTION OF LINE SIGNATURES The main topic of the investigation was the line signatures. A special method for area-shaped signatures was also developed but is not presented here. The methods for the extraction of line objects were originally developed to extract line objects from aerial images [1, 2]. While retaining the principles, details of the extraction methods had to be adapted to the specific features of cartographic signatures. The folios can be regarded as quasi-binary images with constant width and gray level of the signatures. But some line signatures have a complicated design, because they are assembled from different parts (e.g. the railway signature). Other signatures (e.g. the signatures of secondary roads in the military edition of the TK50) show gaps at regular intervals. Two extraction methods developed (local and regional method) are used for different types of line signatures. The main characteristic of the methods is their sequential and object guided manner. The input data are not processed systematically and exhaustively, but the calculations are concentrated on promising parts of the scanned folios. This is achieved by following the signatures. The extraction methods have to distinguish between the different types to avoid the change over from one signature to another. For the start of the extraction, starting points have to be chosen either interactively or by an automatic search, which must ensure that starting points are detected on the signatures of interest only. By the analysis of the map legend the parameter values describing the relevant cartographic features can be derived automatically. For the extraction of roads, the red master or color folio is used which contains the signatures of the roads. While the highways and the main roads are drawn as full lines with different width, the secondary roads in the military edition of the TK50 are represented by dashed lines with gaps at regular intervals of nearly the same length as the drawn line segments. Figures 2a--f show the extraction of the main and secondary roads of Fig. lb. First starting points for the main and secondary roads are searched for automatically along several sample lines (image lines or columns), which are indicated by the white marks at the left border (Fig. 2a and b). Starting from the top starting point of the main roads, the local extraction m e t h o d follows the signature step by step [5, 6]. In every step a sample line shaped like an arc is defined and evaluated to detect the next object point. Figure 2c shows an example for one step of the local method. At the left border the gray level diagram along the sample line is displayed. One can clearly recognize a short interval with low gray levels, indicating the profile of the cross-section with the line signature. In Fig. 2d all main roads are extracted by using only the first starting point. During the extraction two intersections with secondary roads are recognized and marked. Starting from these intersections, the secondary roads are extracted in a second processing step (Fig. 2e). For the extraction of the t w o secondary roads in the upper half of the image, the respective starting points shown in Fig. 2b were used (see Fig. 2f).
158
The signature of the railways is contained in the black master folio (see Fig. lc). The extraction of the railway is difficult due to t w o reasons: The signature of the railway is not drawn as full line and the other numerous signatures contained in the black master folio disturb the extraction. Embankm e n t signatures are n o t separated from the railway signature and falsify the profile of the object cross-section, signatures of stations interrupt the line, in the neighborhood of stations and switches the filling of rectangles is often
a>
b)
c)
d
e)
f)
Fig. 2. Extraction of main and secondary roads.
a)
b)
Fig. 3. Extraction of the railway signature.
c)
159 missing, etc. Due to the complicated design, two different degrees of image resolution are used for the starting point search. At first candidates are searched for in the coarser resolution, then these are examined in a finer resolution to decide on acceptance or rejection. Figure 3a shows the starting points f o u n d for the railway of Fig. lc. The regional extraction m e t h o d [5, 6] is used for the extraction of the railway. It follows the signature step by step. In every step a rectangular sample area is defined and evaluated to detect a complete segment of the line (consisting of a black and a white signature element; see Fig. 3b). Figure 3c shows the final result of the railway signature extraction. The signatures of the waterways are extracted by the local extraction m e t h o d from the blue master or color folios. COOPERATIVE EXTRACTION OF LINE SIGNATURES The simultaneous use of maps and aerial images of the same area results in advantages and new application possibilities for the extraction methods [4, 5, 6]. The processing of several images with different geometries requires an image registration. In the procedure presented, a projective transformation, which needs four pairs of control points, is used. If the area imaged is not planar, we get deviations from the correct locations, which are taken into account by the extraction methods. For the cooperative extraction two extraction modes are available: the supporting and the verifying mode. The extraction is performed in the first image as long as new results can be found, then the results are mapped into the other image. In the case of the supporting mode, the results are accepted as final results for both images. To continue the extraction, the dead ends (end segments of the object parts found) are centered (shifted to the correct location) if necessary. The extraction continues at the centered dead ends. It stops, if an object part of a predefined length has been extracted. The results are mapped back into the first image, the extraction is continued, etc. If the verifying mode is chosen, the results mapped to the other image are taken as preliminary results for both images. The verification process tries to find the respective object parts by means of a modified regional extraction method. Therefore, chains of sample areas are positioned along the mapped results. Only objects, which can be found in both images, are accepted as final results. Objects, which can be found only in one image, are presented to the human interpreter as possible changes. Starting from the dead ends, which have been produced by the verification process, the extraction process can be continued. Thus extraction and verification are performed alternately in both images, until no continuation can be found in either image. The following figures show examples of cooperative extractions from two images, the red color folio of Fig. l d and an aerial image of the same area (Fig. 4c). The main and secondary roads are extracted from the folio and the aerial image either supports the extraction (Fig. 4a--f) or is used for verification (Fig. 5a--f). In Fig. 6a--f the results are displayed when starting the supporting cooperation in the aerial image.
160 Figure 4a shows the starting points which have been found for the extraction of the mare roads. In Fig. 4b the result o f the extraction of the main and secondary roads is presented, where only one o f the starting points was used. The extraction stops at two locations on the secondary roads, because several c o n t o u r lines, which lie close together, or a sharp bend in a gap of the signature, make an unambiguous continuation impossible. Figure 4d shows the extraction result mapped into the aerial image. In Fig. 4e the extraction has been continued after the centering of the dead ends until road segments of an interactively defined length have been extracted. The result is mapped back into the folio, the dead ends are centered and the extraction is continued up to the border (see Fig. 4f).
a)
e)
f)
Fig. 4. Example of a cooperation : the extraction from a color folio is supported by aerial image information. While in the first cooperation example the aerial image was only used to support the extraction, it is used in Fig. 5a--f for verification, too, In this way locations are detected at which the cartographic information does not (any longer) match the information o f the aerial image. As in the first ex. ample the extraction starts in the red folio and the result is mapped into the aerial image for verification (Fig~ 5a). In Fig, 5b an intermediate result with a sample area is displayed, in Fig, 5c the final result of the verification is presented. To get more results the extraction is continued in the aerial image,
161
see Fig. 5d. The new extraction result is mapped back into the folio (Fig. 5e) and verified (Fig. 5f). This is the final result of the cooperative extraction, because continuations, extracted from the folio, cannot be accepted for the aerial image. The last figure shows an example for the support of the extraction from an aerial image b y a map. In Fig. 6a the result of the extraction from the aerial image is displayed. The result is mapped into the folio (Fig. 6b) and the dead ends are centered. Then the extraction is continued for a predefined distance (Fig. 6c). The two lines, which have been found on walkways in the aerial image on the right-hand side, have the same appearance as the roads, b u t they are not contained in the red color folio and therefore n o t continued. Figure 6d shows the result mapped back into the aerial image. Figure 6e and f represent the final result of the cooperation for the aerial image and the folio.
a)
b)
c)
d)
e)
f)
Fig. 5. E x a m p l e of a c o o p e r a t i o n : the e x t r a c t i o n results from folio and aerial image are alternately verified.
CONCLUSIONS The process of digital map generation can be significantlyspeeded up by the automatic extraction of cartographic signatures. Methods are described
162
a)
b)
d)
e
c)
Fig. 6. Example of a cooperation: the extraction from an aerial image is supported by cartographic information.
which are useful tools for the interpreter-guided extraction of line signatures. An aerial photograph o f t h e same area as the map is used t o improve the performance o f the methods. Interactions are reduced to a minimum to supervise the process and assess the results. Usually the results are complete and reliable some minor additions m a y sometimes be necessary, but in most cases, no corrections are required. The m e t h o d s developed are n o t restricted to the digital map generation but can also be used for the evaluation o f multlsensor image data. This includes t h e evaluation o f image pyramids {e.g. for the extraction of objects of largely different width), multitemporal data {e.g. for change detection) and stereo images {e.g. for parallax calculation along line objects). ACKNOWLEDGEMENTS The authors wish to thank P. Fritsche, H. Mayer, R. Neu, P. Schlippe, and Dr. M. Sties, who were involved in this investigation.
163 REFERENCES 1 Groch, W.-D., 1982. Extraction of line shaped objects from aerial images using a special operator to analyze the profiles of functions. Computer Graphics and Image Processing, Vol. 18. Academic Press, New York. 2 Bausch, U., Groch, W.-D., Kestner, W. and Sties, M., 1980. Teilautomatische Objektextraktion aus Luftbildern und Landkarten. Nachr. Karten- Vermess., Reihe I: Ori~inalbeitr~ge, 81: 7--21. 3 Groch, W.-D., 1982. Automatisierung bei der Digitalisierung grossmaszst//blicher Karten. Nachr. Karten- Vermess., Reihe I: Originalbeitr~ge, 89: 41--48. 4 Lubkowitz, R., 1983. Kooperative Linienextraktion aus Luftbild und Karte. Nachr. Karten- Vermess., Reihe I : Originalbeitr~ge, 92 : 73 --82. 5 Groch, W.-D., Lubkowitz, R., Schlippe, P. and Sties, M., 1982. Automatisierung der Extraktion kartographischer Signaturen aus Landkarten mit Unterstiitzung durch Luftbilder. Teil 1 : Uberblick iiber die entwickelten Verfahren und Ergebnisse. FIM-Bericht No. 104, Karlsruhe. 6 Groch, W.-D., Lubkowitz, R., Schlippe, P. and Sties, M., 1982. Automatisierung der Extraktion kartographischer Signaturen aus Landkarten mit Unterstl]tzung durch Luftbilder, Teil 2: Technischer Anhang. FIM-Bericht No. 106, Karlsruhe.