Interpretability of scanned aerial photographs

Ninth United Nations Regional

Cartographic Conference for Africa

Addis Ababa, Ethiopia

11-15 November 1996

Distr.: LIMITED

ECA/NRD/CART.9/ETH.5 October 1996

Original: ENGLISH

INTERPRETABILITY OF SCANNED AERIAL PHOTOGRAPHS

" Interpretability of Scanned Aerial Photographs"

A Paper to be Presented to the Ninth UN Regional Cartographic Conference for Africa

11-15 Nov. 1996, Addis Ababa

By Girma Messelu

Ethiopian Mapping Authority

•• Tha m«icn at thi. paper is pxmmentad at the lBth ISPRS eongr*» in Vienna.

Interpretability of Scanned Aerial photographs**

Girma Mcssclu, Ethiopian Mapping Authority, Ethiopia

K.Tempfli, K.A.Grabmaier, R.Ackennann:Scientific Staffs, TTC, Netherlands

Abstract

Photogrammetry is in transition to the digital world.

Using seamed aerial photographs or other digital images (ifavailable) offers the prospect ofautomating photogrammetric production. While automatedfeature extraction isJust at its utfancy, automation of ortliophoto production has reached operational maturity already.

In titis research digital aerial images are testedfor their information content varying the pixel size and the pfioto scale both in stereo and mono observatiom considering specific and criticalfeatures requiredfor topographic maps at the scale of1:50000 in Southern France. The 1:60000 and 1:30000 photo scales were used for the investigation.

On the basis ofthe results of the tests and tfieir analysis and under all conditions of die experiments, tlie stereo observation tests show that interpretabiHty deteriorates aspixel size increasesper givenphoto scale. In general the interpretability ofdigitalphotograplts isfound to be inferior than of their analogue ancestors. Even, the 1:30,000 and 15pm image could not recover the content (criticalfeatures) ofthe originalphotograph. Among the tested constellations ofphoto scale and pixel size, the 1:60,000 and 1 Sum and the 1:30,000/'30am images have small difference in the interpretability of critical features and are recommendedfor 1:50,000 mapping

withjurtlier investigation on economic aspects.

Regarding the orihophoto observation tests, the interpretability is mainly governed by the limit of the eye resolution. Within the context of the conducted experiments, SOftm pixel size (2.5m) orthophoto from l:60,000/30fim image seems the best choicefor 1:50000 mcfpmg. Whether even smaller scale and/or larger pixel size can be used should be the subject to further investigations under local conditions.

1. Introduction

The major problem of developing countries, in the geo-information world, is the slow speed of topographic map production. Most of these countries have no full coverage of their territory at their most basic mapping scale of 1:50,00c). Even

most ofwhat has been mapped needs map revision.

Those problems put mapping agencies in a great challenge with the growing demand of a geo- information base for natural resource investigation and development planning.

At present the most important development in photogrammetry is the transition from analytical to digital photogrammetry. Scanned aerial photographs are the main input of digital photograrnmetry. Digital frame cameras are not yet available with the same order of resolution, image quality and amount of data as aerial cameras.

Satellite images (eg. Spot) do not offer sufficient resolution for large and medium scales, and so to use aerial photographs instead of satellite imagery oflers more flexibility in digital photogrammetry.

We can choose the image scale and the pixel size as required.

Photo scale and pixel size are the basic parameters for accuracy and interpretability of digital images.

In choosing appropriate pixel size, the limiting factors are computer storage requirement, image quality and ground resolution required for specific application, photographic system resolution and speed of operation. The most cost-effective solution can be expected from minimum photo scale and maximum pixel size of scanning.

However, lowering the photo scale and increasing the scanning pixel size reduce accuracy and interpretability. Therefore, the minimum scale and the maximum scanning pixel size should be determined by the required accuracy and interpretability of digital images and the ortho/image maps.

The influence ofpixel size on accuracy has already been subject of research. But, its influence on interpretability, in relation to photo scale and its

consequence in economic terms has not been thoroughly investigated yet.

2. Experiment on the Interpretability of Digital Aerial Images (scanned photographs)

Interpretability of scanned aerial photographs is influenced by many factors. But, in these investigations only two parameters, viz., pixel size and photo scale in conjunction with a specification of 1:50000 topographic mapping were considered.

In all the tests, visual interpretation method was used. Hence, the human factor plays a significant role. To cope with differences between interpreters and to minimize the learning effect about the scene to be interpreted, 3 persons are involved in the interpretation of the digital images.

Stereo-observation test: The test was studied in stereo the softcopy input (of different pixel sizes) and hardcopy input using two photo scales. The primary aim of this experiment was to investigate the efiect of scanning in relation to photo scale on interpretability limited to a specification of 1:50000 topographic mapping.

Mono-observation testln this experiment comparative study was carried out on the interpretability of orthophotos produced from digital aerial images s»canned/aegregated at different pixel sizes using two photo scales. From this experiment, a photo scale and a pixel size that was likely to be the best combination for 1:50000 orthophoto mapping was selected within the scope of the research.

2.1. Test area, material and equipment

The test area is located in Southern France at Cavaillon. It covers about 6km by 7km (42km') and is covered by variety of land-cover. A variety of topographic features such as built-up areas, scattered buildings, different classes of roads, hydrological, cultural feature, etc., are found in the area. Therefore, it is of a mixed type area (urban and rural). It also consists ofboth mountainous

and Hat areas. Elevations range from about 100m to about 660m.

Input data:

- B/W aerial photographs at the scale of 1:60000 and at 1:30000.

- Digital aerial images of the 1:60000 and 1:30000 scales scanned at 15um.

- Coordinates and sketches of control points in the test area.

Different hardware and software were used for the tests.

Equipment: Planicomp,TraterT10, etc

Software: Kork, Demeter, rotation, Arc/Info, etc,.

2.2 Experiment design considerations

Critical features are selected on the basis of size, contrast, and appearance similarity. The legend of the map of the test area at the scale of 1:50,000 was used for the selection .

The original photographs were scanned at pixel size of 15um. But, estimation of appropriate pixel sizes was made based on application requirement resolution of photographic system and storage requirement for the stereo-observation test.

Regarding the mono-observation test, the resolution of the eye was the critical factor in the selection ofpixel size ( for the interpretation of the orthophoto by an unaided eye). By taking for granted that of Doyle's idea, the resolution ofthe eye was taken to be from 50um/pixel to lOOum/pixel and is equal to 2.5m/pixel to 5m/pixel at the presentation scale of 1:50,000. This proves that additional source of information is essential to produce orthophoto maps from the orthophotos if we accept the ground resolution of 2m/lp to 3m4p for 1:50.000 mapping (Doyle, 1984 V Hence, the orthophotos were printed with pixel sizes of 50um (2.5m) and IOOum (5.0m) governed by the eye resolution. With regard to the input pixel sizes of 30um (1.8m) and 60um (3.6m) at the photo scale of 1:60000, and a pixel size of

60um~ (1.8m) at the scale of 1:30000 were

considered.In general, this experiment was carried out prior to the stereo-observation test to minimize the human expectation during interpretation.

In a series of interpretational tests (of the same area), the human expectation can help in identifying features if a single operator is involved in consecutive interpretations. In the present experiments, a single operator was involved in more than one interpretation o( the same area.

Therefore, much attention was given to minimize the influence of expectation while designing the sequence of the tests.

2.3. Interpreting the stereo models

Based on the convention for the interpretation and the restricted sequence oftests, each operator had collected the data for the part of the area in wliich he was assigned. The single lane roads were digitized first to enable systematic digitization (patch-wise) for other features. Detection and identification of features were carried on with great care based on the organized interpretation key.

The data was collected using Demeter mapping software. The surveys which covered completely die test area were converted from demeter to DXF file format. The conversion from Demeter to DXF format was performed on Traster T10. The DXF files were transfejred to Arc/Info via network On the Planicomp, the collection of data was basically the same as on the Traster but the data conversion was first to DGN file then to AraTnfo.

2.4. Interpreting the orthophotos

Prior to interpreting the orthophotos, it was necessary to do some processing in order to produce the orthophotos. This includes generating DTM, generating the ortho-iinages, image enhancement & writing on the film, and development and contact printing on paper.

Generating the DTM: The DTM was generated automatically from each image with a ground resolution of 5m except for 1:30000, 60j.un image.

In this particular case four models were used to produce DTMs to cover the test area (using three levels in image matching and 2.5m ground resolution).

Ortho-images: These were generated with ground resolutions of 2.5m and 5m as planned. It was not

possible to generate ortho-image for the whole area defined by the window since the test area was falling near the edge of model/models. The time consumed for generating DTM and ortho-image is

i in the table 2.1 for an indication.

Critical features were digitized on microstation from the orthophotos. The working condition of digitization on microstation was not suitable for the operator. It was hard to digitize the small features at the small scale of 1:50000. The shadow of the fingers together with that of the digitizing tablet has strong influence. Because of these conditions, the operator could not digitize all the features that he teould identify when looking straight ahead. The point features were liardly identified. It was sometimes possible to detect house-like features but difficult to identify them as small and isolated houses.

Regarding the quality ofthe orthophoto, the pieces of orthophoto produced from 1:30000, 60 um images were found bad. They were milky manifesting less contrast between different features.

2.5. Methods of Evaluation of Interpretation Results

Reference data: Normally, the reference data should have been the ground truth (map compiled with field completion) since it is the higher level source ofgeo-information. This might have led us to hiow the amount of information to be collected from the field in order to complete the 1 ;50,000 scale snap. If it was so, we might draw realistic and relative cost comparison between different inputs.

The 1:50,000 map of the test area was not used as a reference data since it was not compiled from aerial photos at hand. It was revised and fainted in 1986 Therefore, the data extracted from analogue photographs were used as the reference data. Each

photo scale was serving as a reference for digital

images derived from it.

Error matrices: Generally, error matrices which can show the number of correctly and incorrectly interpreted features in realistic units are organized.

Omissions (features wrongly excluded from a particular feature category) and commissions (features wrongly included into particular feature category) were also identified.

Input DTM/Ortho-image resolution (m/m)

Scale Pixel size(.um) 5/5 5/2.5 2.5/2.5

1:60000 1:60000 1:30000

60 30 60

24mn 08s

26nin 34s 32nin 52s

76mn 52s

Table 2.1: time consumed in generating DTM and Ortho-image.

A total of 30 error matrices arc tabulated for the two experiments ( one per each feature type and per test) in the form of table 2.2.

Statistics:

1. Producer's accuracy (PA) (interpretation accuracy)

PAl = ( X, / X., )K>0%. It shows the probability to detect and correctly classify a feature.

Omission O,» ( 100 - PAj >%

2. User's accuracy (feature accuracy)

UA| - ( X, / X,. jlOO%. It shows the probability that an item that was interpreted as A is actually A.

Commission Ct - (100 - U*.)%

3. Un-detection rate ("absolute omissions", missed items irt %)

Detected but misclassified item*.

4. Overall producers accuracy (OPA) OPA =( n X, /X.. .1100%

5. The X, for i not equal to i represents die

commission error.They arc of interest for a detailed analysis between classes in which the highest confusion occurs. Conclusion can be drawn to change the specification (if possible) to reduce cost (e.g, do not distinguish foot paths and tracks, too expensive because too much field verification is needed).

The statistical parameters:

PA gives a global idea about interpretsbility per feature.

OPA gives an overall interpretability figure pet feature type by taking sample size into account.

M is a good measure for the relative amount of field completion required per feature. UA and F are measures for misclassificatious, thus the amount of field verification is required.

3.0. Results of Experiments

3.1. The stereo observation tests: The confusion matrices of all the test cases were tabulated. But, for the present case the derived interpretation accuracy values are compiled in table 2.3.

Point features: Isolated and small houses were the most frequent features. Their producers' accuracy varied from 24% to 54% for 1:6O,O00

scale and from 38% to 68% for 1:30,000. The success rates of bridges are in all instances higher (except 1:60,000/l 5um case) with a maximum of 82 °/b at tlie scale of 1:30,000 with 15 urn pixel size.

The explanation for that can be the location of bridges (intersection of line features) aids the interpreters to detect and identify them.

Monuments are the most critical features which are neither identified in the original photographs nor in the digital images of the two scales. The second most critical features for most of the images are ttlie towers (with 0% success rate).

Interpreted data

A B

Z Missed Total

Reference data ■

A x X.

xM

Xm

B X, X-

X..

z

r-A,B Z

X]fn^n

Total

X,.

X..

Table 2.2: Concept of the error matrix.

In the two images i.e. 1:30,000/ 30 pirns and 15 pirns, they have got a success rate of 17% and of*

100%, respectively, They are fully missed in odier images except at 1:30,000V 60um image in which one tower is misclassified as a small house. The success of interpretation of reservoirs is similar to that of towers with the difference that they have got a success rate of 100% at l:3O,OOO/3O^m image. Also their user accuracy is 100% at 1:30,000/15pm image while that of towers at the same image is 50%.

Line features: Foot paths are more difficult to identify than roods; they yielded a success rate of 0 to 66% at 1:60,000 photo scale and of 77% to 85% at the scale of 1:30,000. They could not be identified in the 1:60,0G0/60um image.

Single lane roads of width about 3m are the most frequent features in the test area Their success rate ranges from 57 % to 78% at die 1:60,U00 photo scale and from 66 %to 85% at the scale of 1:30,000.

Single railway is always identifiable while the higli tension power line is neither identified in the original photographs nor in all the scanned images.

Ditches have got a minimum success rate of 0% at 1:60.000/60um image and a maximum 86% at 1:3O3OOO/l 5 jun image. They have a special

property ,i.e., a thin line of vegetation at their edges that can aid operators to detect their location. But, the vegetation cover can also cause difficulties in following the drain line themselves.

Because of this peculiarity, the result for ditches were better than those of fool paths. Exceptions are the high success rates of foot paths at 1 ,30,000/60um images.

The most critical features are higli tension power line:*. Foot paths are the second most critical featuies, Ditches are also critical but less as compared to foot paths because their users' and prodixcers' accuracies are higher than those for the foot paths in all rind in most of the cases, respectively.

Area features: The success rate of vineyards ranges from 0 % to 58% in the images from 1 -60.000 and it is between 36 % to 95% in the uuages from 1:30,000 photo scales. Producers' accmacy for orchards varied from 17% to 55% in the images from 1:60,000 scale photo and from 63% to 87% in the images from 1:30,000 photo scale.

Even though they have less area coverage, cemeteries are found to be the most critical area features and were only identified at 1:30,000/

15um image. Orchards have got the higher success

photoscale pixelsize(j.im) Feature Isolatedhouses Bridges Towers Reservoirs OPA Footpaths Singlelaneroad Singlerailway Uirches OPA Vineyard Orchard Cemetery OPA

1:60000 60 PA 24 44 0 0

UA 16 50 u.a n.a

M 76 56 100 100 26 0 57 0

n.a 66 100 n.a

100 43 0 100 47 0 17 0

a.a 29 a.a

91 83 100 6

30 PA 36 43 0 0

UA 23 75 n.a n.a

M 64 57 100 100 35 55 64 100 73

81 53 10O 100

45 35 0 27 66 31 46 0

63 54 n.a

62 52 100 42

15 PA 54 46 0 0

UA 45 67 n.a n.a

M 46 54 100 100 47 66 78 100 79

51 71 100 73

34 2? 0 21 78 58 55J 0

71 80 aa

42 40 100 57

1:30000 60 PA 38 60 0 0

UA 33 100 n.a n.a

M 62 40 86 100 36 77 66 100 58

67 90 100 90

16 32 0 42 67 36 63 0

99 63 n.a

62 36 100 46

30 PA 56 67 17 100

UA 33 67 100 100

M 44 33 83 Ci 55 86 79 100 81

94 95 100 100

14 21 0 19 80 91 90 0

91 47 n.a

4 6 aoo 91

15uiti PA 68 82 100 100

UA 40 75 50 100

M 32

18 ;

0 0 70 85 85 100 86

82 92 100 100

15: 15 0; 14

'■■ 86

95 87 100

90 75 100

4 11 0 95 Table2.3:Summaryoftheresultofstereoobservationtests

rateofinterpretabihty at the maximum pixel size than vineyards but the difference is not well pronounced At a maximum, 5% of orchards are misclassified as vineyards, and 9% of vineyards as orchards.

Vineyards cover most of the test area. They had the upper hand on area features as houses and single lane roads had on point and line features, respectively. At .the maximum 91% of the vineyards is undetected (1:60,000/60um image).

The minimum un-detection rate is 4% at 1:30.000,30 pirn and 15um images. A peculiar result from area features is that of vineyards in 1:60,000/60um images (0%).

3.2. The mono observation tests: In addition to the orthophotos to be produced according-to the experiment plan, another orthophoto is produced with 20um (lm "ground pixel size") from 1:60,Q0G/l5um images. This is to check whether there will be an improvement in inteipretability with a smaller pixel size than the assumed minimum eye resolution (10 lp/mm). The data was processed on Arc/Info using the same procedure as for the stereo observation test.

Point features: They were the most critical features, Generally, they could hardly be identified in any of the orthophotos. Buildings can be detected based on the context of other features. For instance, the end of a private road signifies that there is a building. But, it is difficult to identify it as an isolated small house. Bridges can also be detected by the situation of a road crossing a river but hardly to be identified.

Therefore, they were not included in the error matrices.

Line features: The producer's accuracy of toot paths varied from 27% to 55% and the users' accuracy from 44% to 60% (see table 2,4).

Regarding single lane roads, their success rate varies from 32% to 67% and their users' accuracy from 47% to 66%.

Ditches have yielded a success rate of 33% to 55% and a users' accuracy between 33% and 77%. Power lines were neither visible in the reference data nor in the interpreted data.

Another line feature is a single railway which is out of the coverage of die orthophotos.

in examining table 2.4 one can see that the most critical line features are found to be the power lines. The second most critical features are the footpaths. About 40 to 73% of them are missed.

A maximum of 5% of the foot paths are also confused as single lane roads. And also, at a maximum, about 3% of the single lane roads are interpreted as foot paths.

Area features: Vineyards have got a producer's and users' accuracy varying between 0% to 46%

and 39% to 63%, respectively (see table 2.4).

The success rate of orchards varied from 33% to 59% and their users' accuracy ranges from 39%

to ?5%. Area features which were not visible in any of the orthophotos were the cemeteries.

Vineyards have a low success rate of 0% in the orthophoto from 1:60,000/60um image. Their success rate in the orthophoto from 1.30,000/

60um (9%) is a peculiar result, even though the results of the orthophoto(2.5m pixel size) from 1:30,000/60 urn is in all instances worse than those of the orthophoto with 2.5m pixel size from 1:60,000/30um

which is equivalent in "ground pixel size".

When looking into the general trend of the results (see table 2.4), the result of the orthophoto with a "ground pixel size" of 2.5m from l:3O,OOO/6Oum images is beyond the expectation when compared to the results ofthe orthophotos from 1:60,000/30um images. The main reason for the bad result of the orthophotos from the 1:30,000 photographs must be sought in the quality of the original image.

4.0. Conclusion and Recommendation Conclusions

The intention of the series of interpretational teMs was to investigate the interpretability of digital aerial images taking the parameters- pixel size and photo scale in conjunction with 1:50,000 map specification. To this end the interpretation of the original analogue photographs were taken as reference. Despite the limitations of the experiments, the following conclusion can be drawn.

- Interpretation of digital images, in most cases, is worse than the analogue ones. Even, the best digital image, i.e., 1:30,000 photo scale with 1 5 um pixel size, was inferior to its analogue ancestor. The total missed items (undetected point line, and area features) was found to be as large as 5 % and the average overall producer accuracy only 83%. The result pertains to the two viewing systems used (i.e., Zeiss Planicomp

photo scale Image pixel

Orthophotd scale

& pixel size (m) Feature

Foot paths Single lane road Ditches OPA Vineyard Orchard

OPA

MM

L:

60

1:50000 5 P A.

27

32

■tt

0

48 0

sans

U A 44

47 43

M

73 68 67

31

n.a

43

n.a

14

100 52

100 aOOOO

1

P A 44

53 52

50000 5

U A 52

66 66

■aw

JO

M

51 44

48 51

26

56 0 ,

61

43

n.a 63 44 100 38

MMDOB

I

P A 55

65 55

37 59 0

:50000 2.5

U A 50

64 67

62 51 55 n.a

M

40 33 45

63

31 100 43

■MB^MM*■MB

15 .

P A 53 67 52

46 52 0

1:50000 1.00

UA

45 51 77

M

47 33 48 63

55

■■■■■■iii43 n.a

48 39 100 47

MBH

1 :3000C 60

1:50000 2.5.

P A ■ 43

40 52

UA

36 48 63 41 9 33 0

39 39 n.a

M

48 59 48

89 59 100 17

TABLE 2.4: Summary of the result of mono observation tests

and Matra Traster Tl 0).

-Among the various constellations studied, which

combination of photo scale and pixel size is the most ffavourable one with respect to 1:50.000 scale map specification of France? Obviously

more tests would be desirable and above all cost

calculations. With the given data we are inclined to state that 1:30,000 with 15um is not

necessarily the best choice, because;

- The gain of 15um instead of 30um at 1:30,000 is not impressive, the main shortcoming of the

3Gum image being a 15% lower producer accuracy of point features;

-The 1:60,000/1 Sum images have the same nominal "ground pixel size" as the 305000/30um images, yet they showed slightly worse results, with the largest difference in the area features.

Although scanning and processing of 30pm images is faster than those of 15um, we doubt that the corresponding cost savings balance the hi^ier costs of the 1:30,000 photography instead of the 1:60,000. Larger photo scale implies

higher cost of acquiring the photography, more

ground control, more model setups, and more

edge matching

- There is a gain of 20% in the interpretability of orthophotos from 5m to 2.5m resolution,

produced from the same image (1:60000/30um).

This gain of interpretability signifies that an unaided eye can resolve details less than 1 OOum.

Hence, the results of the tests are in agreement

with Doyle's (1984; investigation on the resolution of the eye and is as anticipated in the

design of the experiment.

-With respect to meeting. the standard of 1:50,000 scale map specifications, the produced orthophotos show deficiency in providing

critical details (point features undetected, narrow

line features correctly interpreted with 60%, small area features roughly 40%y If such a low mteipretabiliry is acceptable in specifications and to the map users, then the digitally produced

orthophoto can directly - serve as a map substitute. Otherwise enhancement to an

oithophoto map must be considered. If smail features such as houses and foot paths must be shown, they could either be compiled from stereo observations (preferably on analytical plotters) or by field completion. Again cost calculations are needed to make a good choice.

-Among the tested alternatives, the orthophoto at

1 ;50,00b scale with the ground pixel size of 2,5m

tion. 1,603000/30um image seems the best

choice. Because there is no considerable

difference between this orthophoto and the

others produced from 1:60,000/15um image. The

chanty in scanning pixel size from 30 pm to

15 urn and the corresponding computation time

tor DIM and orthophoto generation would cost

more than the cost to include the slight

difference in the interpretability. This statement

can be strengthen by the investigation made by Bahr and Wiesel (1991). They said that the change

of scanning pixel size from 25 um to 50um would

reduce the production time of digital orthophelo

^including scanning of the original photographs) fiom4.5hto 2.375h. The statement can serve as an indication for the existence of considerable difference in production time of digital oithophotos between 15 urn and 30um pixel size. Any how, again cost, analysis is a crucial factor to give firm conclusion.

Recommendations:

It is emphasized that the above conclusions ate

only indicative and that more extensive testing will

help to draw firm and general conclusions. On that basis:

- Knowing the exact resolution of the original photographic system is crucial,

- Further investigation on cost for different pixel size and photo scale combination towards interpretability must be carried out. For that the

map of the test area must be used and there must

be knowledge on cost of field work required to oonplete the data from the analogue photographs;

- Specific tests in local environment are of great importance as objects may have different characteristics. Other factors such as the viewing system can also be different. Training of operators might not be necessary for local tests, bui.

monitoring them in compliance to a convention made is essential.

-Mapping authorities which are about to introduce new techniques for mapping should realize that, the present state-of-the-art, it is of advantage to use analogue/analytical plotters, if available, rather than digital plotters for feature extraction.

-ivtmufecturers of digital photogrammetric systems should take into account the increase in the production speed and reduction of the storage problem to make small pixel sizes attractive (cost- effective), specifically for feature extraction.

References:

Bahr,H.P. and WieselJ. (1991): "Cost- benefit Analysis of Digital Orthophoto Technology" Digital

photogrammetrics systems, Wichman, Kalsruhe. Germany, 1991.

Doyle, F.J; (1984): "Surveying and Mapping with Space Data"; ITC

Journal, No. 4.

Leberl,F.W.,< 1992)"DesignAlternatives for Digital Photogrammetric systems" ,ISPRS; comm.ll, v.29;

Washington.

Meier H.H.; (\ 984): "Progress by Forward Motion Compensation in Cameras" ;Proceedings 15th ISPRS. v.25; part Al.

ScliieweJ. and Siebe,E.; (1994): .

"Revision of Cartographical Databases using Digital

orthoimages", ISPRS, comm.IH, v.30, part 3/2; Munich.