reduction of dsm to dtm and quality assessment

7/23/2019 Reduction of DSM to DTM and Quality Assessment

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Protogrammetry and Remote sensing Semester Project.

DSM to DTM generation - 1 - 5/29/2008

PHOTOGRAMMETRY AND REMOTE

SENSING PROJECT

Project Name: Reduction of DSM to DTM and Quality Assessment

Department: Institute of Geodesy and Photogrammetry

Focus Area: Zurich Airport

Product/Process: Aerial Photogrammetry with RGB and CIR.

Mr. Jedsada Kerdsrilek

Geomatic Engineering and Planning MSc

Swiss Federal Institute of Technology Zurich


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TABLE OF CONTENTS

1 INTRODUCTION ................................................................................................................................ 3

2 IMAGE PREPROCESSING ............................................................................................................... 6

2.1 Separated Images Band............................................................................................................. 6

2.2 Filtered Image ........................................................................................................................... 8

2.3 Preparate Orientation Parameters.............................................................................................13

3 GENERATION OF DSM WITH IMAGE MATCHING.................................................................15

4 FILTERING OF DSM FOR DETECTION ON-TERRAIN OBJECTS .......................................20

5 INTERPOLATION OF GROUND POINTS ...................................................................................25

6 QUALITY ASSESSMENT.................................................................................................................26

7 CONCLUSIONS.................................................................................................................................30


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1 Introduction

The Project of DSM to DTM generation is an important process to make anaccurate DTM, specially on this project. We have produced accurate DSM with extracted

object features in Zurich airport. In order to automatic pilot landing and take off the

aircrafts, the project can be separated in four steps;(i) started with image preprocessing by applying two kinds of filter to reduce the

number of noise in the images and increase the contrast of feature objects, in

order to perform a quality of image matching process,

(ii) The second step is DSM generation with image bundle adjustment.(iii) DSM filtering is a process to filtering and eliminating on terrain objects in

order to reduce DSM to create DTM.

(iv) Finally it can interpolated the eliminated area with interpolate function to

generate on ground surface of DTM in automatic way. However, we canevaluate the product of DTM and of Lidar data. The comparison between both

surfaces is completed.

The used data in this project are aerial images and metadata of images as shown

in table 1

Film Color and CIR

Camera Type Frame

Focal Length 303.811 mm.

Scale 1:10,150 (RGB)

1: 6,000 (CIR)

Endlap 70%

Sidelap 26%

Scan Resolution 20 micron

Table 1.1 Image data description.


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The flowchart 1 is a brief process description of the project. Starting with Image

preprocessing step, which decide the mostly contrast band from RGB image or CIR in

order to know which band is the best contrast. After select the situation band, weextracted the contrast band from all images (22 images) and started to reduce the number

of noise by image filtering. Image filter can be done by recheck the results and comparing

the histogram before and after generation. As for the parameter of camera calibration andexternal orientationare prepared in separate text file format, the task of this part is to put

all of parameter together in ORI format (parameter format of SatPP) for image

orientation process.

The DSM is generated by using SatPP software. Firstly, seed points distributed

every part of the image are meausred. Every image pairs will have at least 10 points of

seed point in to set the X-parallax. Then we generate accurate DSM including terrain

objects. The next is to reduce DSM to DTM generation to extract terrain objects andinterpolate surface model with SCOPP++. Finally we can check quality of DTM by

comparison to available DTM from LIDAR data.

Diagram 1.1 The working process of DSM to DTM generation.

2. DSM Generation with Image Matching

- seeds points measurement

- bundle adjustment- DSM generation

3. Interpolate DSM to DTM

- filter parameters assignment

- DTM generation

4. Quality Assessment

- Lidar DTM assessing

- comparing both surface DTM

1. Image Preprocessing

- choose the contrast band from RGB or CIR

- separating contrast band from all of theimages project

- images filtering

- parameter preparing with image orientation


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The figure 1 shows the example of aerial images that use in this project withacquisition in Color and CIR data mode. The project area is around Zurich airport with 44

images and orientations are available.

RGB Images

CIR Images

Figure 1.1 Aerial images acquisition in RGB and CIR mode.


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2 Image Preprocessing

Image Preprocessing is a significant process to reduce the effect of the noise andincrease quality for the next step process. The result will be suitable in term of

visualization for the matching process.The process started with reduction of radiometricproblems. For instance, strong brightness area or dark regions are reduced with filteringtool such as noise filter to smoothing roughness radiometric area and wallis filter to

enhance and sharpen the objects. Before the filtering process, we should design which

available channel of data is the most suitable to be generated DSM.

2.1 Separating Image Bands

Image channels separated with Software Photoshop in order to design that which

band is the most strongly contrast image. We stared to choose the example image with inRGB and CIR to extract Image RGB red, Image RGB green, image RGB blue, image

CIR IR, image CIR red and image CIR green. With visualization, we can see that the nearinfrared channel(NIR) and red channel(R) from CIR image are the most contrast image

channel. In this project, we designed to use red channel from CIR image for generate the

DSM. Though we selected very contrast band, there are still a radiometric noise whichcan show in the histogram configuration in Figure 2.1 and Figure 2.2 .

Separate Band RBG

RGB Band Red

Figure 2.1 Separate RGB Image and the histogram generated

RGB Band Green RGB Band Blue


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Separating Band CIR

CIR Band IR

Figure 2.2 Separating CIR Image and the generated histogram

As for the histogram showed that the histogram gray scale curves is not smooth,because there are the radiometric problem and the black frame around the image are exist

as explained above. So the next step is cutting the frame to reject the no data effect andfiltering image with NOISE filter and WALLIS filter to reduce the radiometric problem.

CIR Band Red CIR Band Green

Noise from radiometric resolution

Noise from ima e


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2.2 Filtering Image

First of all, we will apply NOISE filter to reduce radiometric problem of the

image with the result are show in figure 2.3 NOISE filter in case of RBG and 2.4 NOISEfilter in case of CIR

Filtering RGB with Noise Filter

Figure 2.3 the result of NOISE filter in case of RBG


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Filtering CIR with Noise Filter

Figure 2.4 the result of NOISE filter in case of CIR


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After NOISE filte, we have obtained the low noise image. The next step is toapply WALLIS filter to enhance the edges contrast of exist objects. The results are shown

in Figure 2.5 WALLIS filter in case of RGB and Figure 2.6 WALLIS filter in case of

CIR.

Filtering RGB with Wallis Filter

Figure 2.5 the result of WALLIS filter in case of RGB


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Filtering CIR with Wallis Filter

Figure 2.6 the result of WALLIS filter in case of CIR


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After filtering image, the result showed that the noise filter can eliminate

radiometry error from the gray scale data which shows on the histograms. On the other

hand, wallis filter can enhance edges of the feature objects. By the way, NIR and Redchannel shows a good reparability for the feature objects. Finally, we designed to use red

channel from CIR images for DSM generation. As show in Figure 2.7, the comparison of

RGB and CIR in case of radiometric contrast to separate feature objects.

Comparing with zoom in to small objects

RGB

CIR

Figure 2.7 the comparison result shows that the red band of CIR is the most contrastimage band.


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2.3 Orientation Parameters Preparation

Before starting DSM generation, we need to prepare orientation file for eachaerial images with the camera parameter and acquisition condition. Normally we will

calibrate the camera before achieve images in order to get calibration parameter and

internal orientation information such as focal length, fiducial marks, calibrationparameters. The parameters are provided in the calibration data of the camera, as show in

Figure 2.8. The Figure 2.8 shows parameter of 8 points fiducial marks of internal

orientation of each image.

Figure 2.8 Show parameter of 8 Fiducial marks in order to generate internal

orientation images


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After calibration the camera we will have internal orientation parameters of the

camera. The external orientation of each image is generated from the navigation andposition of instrument that located and connected with the camera on airplane. All of

parameters are provided in format SUP file, within Sup file which external orientation

parameter such as Xo, Yo, Zo and,, are provided.

The next step is to generate rotation metric from the rotation parameter withchanging the unit. The unit are provided in RADAIN to GON format and using Rotation

Metric software to generate rotation metric form. The last parameter is affine transform.

For this case, we used LPS with internal orientation by mark fiducial 8 point andmeasurement tie point in overlap area in order to generate external orientation with

blunder adjustment create affine parameter for each images.

Finally we can form orientation file in format ORI file as show in Figure 4. The

ORI file is a form of orientation parameter for using in SatPP software and the form ORI

are provided internal orientation with focal length. Meanwhile, affine metric for each

images and the ground coordinates Xo, Yo, Zo which rotation metric ,, areprovided for external orientation.

Flowchart 2 ORI file generation from SUP file and camera calibration file.

Rotation Metric software

External Orientation Metric

- Orientation ,,

SOCETSET

Orientation ,,Change RADAIN to GON format

Ground Coor. Xo, Yo, Zo

LPS software

-measurement fiducial marks

-measurement tie points

Calibration

file

Affine Transform Metric


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3 Generation of DSM with Image Matching

For the result of Image preprocessing, it shows that the NIR and Red channel of

CIR is most appropriate channels for images matching. We have chosen red channel for

DSM generation in SatPP (Satellite Image Precision Processing). The DSM generationprocess started with transform images format JPG to RAW (SatPP format) and then

created the project with all image input covering the airport area. Generally, we save ORI

file (orientation provided) and the image data in the same directory in order to link themtogether.

In general, aerial photo obtains the frame work for marking fiducial marks in

order to form internal orientation of image, but we have orientation file from previous

process already. Thus, mark tool is applied for all image frame work so that non spatialinformation can be recognized in the software. The next step is measurement the seed

points to get height relationship between image pairs.

The diagram below shows the SatPP work flow (Gruen A., Kocaman S., Wolff

K., 2007: High accuracy 3D processing of stereo satellite images in mountainous areas).Image processing, as also as shown in chapter 2, has been completed at the first step and

triangulation for external orientation has been done in sequence. Image matching is

generated the points cloud on the terrain surface and finally, least square matching are

provided to improve the result statistically.

Figure 3.1 Work flow of SattPP processing. (Gruen A., Kocaman S., Wolff K., 2007)


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In case of SatPP, matching algorithm composes of three main techniques tosearch for matching features. First, points correlation matching is done by checking

corresponding between image pair. After that, grids matching to check in grids by grids

are carried out and finally the edge matching to judge against the same object in the pairimage is determined. All matching algorithm of SatPP are respected to separate image

area in order to reduce the time for image matching.

As image matching results, we can see that the forest area is in green points

because they have a unique prattle. Therefore it can be simply done in matching process.

In case of construction area, the result is not as fine as the forest. The buildings in

construction area are good with edges matching and the road is a flat area without theprattle or texture so the result is show on yellow points. Also in some part of the structure

area, there are red points because the object is moving or the object has no texture and

less correlation.

DSM generation is the last step for DSM generation process. The result of DSM

generation is shown in Figure 3.2 to 3.5. The figures show the interesting area andcomparison the result of the matching points and the original images in order to see how

the relationship of the process generated the results. In Figure 3.2 represents all

interesting cases such as buildings, roads and the forests, and we will see in the detailbelow.

Figure 3.2 The result of DSM generation showing case over Zurich airport.


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DSM in case of the building shows that the airport building can be reconstructionwell, they can getting the relation height between the structure and rebuild even in case of

airplane.

Figure 3.3 DSM generation in case of building area


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DSM generation in case of road reconstruction is show on the matching in yellowcolor and its can generated well like the building with pretty smooth surface.

Figure 3.4 DSM generation in case of road structure.


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DSM in case of forest shows that the forest are can be generated with differentheights according to the color of them.

Figure 3.5 DSM generation in case of forest area.


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4 Filtering of DSM to Detection on Terrain Objects

The goal of this process is to determine terrain objects such as building, forest,and other object features on the ground. In order to produce DTM from DSM, we

concentrated on ground classification and the data should be eliminated the entire object

on the terrain surface.

We are using Scop++ algorithm for generate DSM to DTM as show in the

Figure 4.1. The algorithm started with searching grid width a long the original pointscloud to classify data with selected LOWEST points. The next is generate approximate

surface from LOWEST points cloud as show on red line and robust adjustment for

eliminate gloss error which are giving more precise terrain surface. Buffer zone area is

used to limit the ground surface from the original points cloud, shown in Figure 4.2.

Finally, the precise surface with only ground points is generated as show in Figure 4.3.More information can be found in K. Kraus, N. Pfeifer, Advance DTM Generation from

Lidar Data.

Figure 4.1 On terrain searching windows


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Figure 4.2 off terrain surface detection

Figure 4.3 on ground surface generation


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As the result of DSM generation, the image size of DSM generation is too big(about 2 gigabyte) and software SCOPP has a limited file size for image filtering. We

reduced the study area by cutting DSM points cloud over the ZURICH airport for DSM

filtering process.

The classification DSM to ground surface on SCOP++ starting with, using the

parameter of filtering object from LIDAR default STRONG with are show the process onDiagram 4.1. The filtering process started with three eliminated building model in order

to classify building object and to separate from ground surface. The parameter of building

elimination has been done by classifying the original image. Classifying is completed by

defining cell size of searching window. Then, defining a minimum slope and minimumarea of on terrain building that can be detected by the searching window and eliminated it

from the source data. The model sought parameter from coarse to fine in order to

eliminate big buildings until small buildings which relate to the slope and the area of the

objects on ground.

The results of building elimination model are the DTM with the gaps ofbuilding that can be detected and eliminated from the provided parameters. However,

there are the vegetation and the object on ground surface left to be removed in the next

process.

We are using iteration of filtering in the second process to eliminate off terrain

objects. The first step is THIN OUT to separate ground surface and off terrain object bydefining cell size searching and getting the LOWEST point to represent on ground

surface to reduce the original source image.

The second is filtering the data by using robust iteration and defining the weight

function which are created average surface. When the point are below the surface, theyare defined as a ground point (weight = 1). The results are called approximate surface

which are limited upper branch by distant limit to find off terrain point and also for lower

branch condition.

The third is surface interpolation by classifying point on previous step with

linear prediction and defining grid size to obtain on terrain detail surface.

The last step is sort out the data by limit upper and lower surface for DTM

generation. In this step, off terrain object are eliminated which the user can define upperand lower tolerance. On ground surface can be generated with four classification stepsand then we can iterate coarse to fine step from thin out to sort out which reduce

parameter and get the better result. The iteration step will provide the parameter by

coarse to fine as shown on the Diagram 4.1. It represents parameter for each loop by thecolor different.


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Diagram 4.1 Classification step of filtering on terrain objects

(1) Eliminate Building

- cell size 3

- min slop 1- min area 12

(2) Eliminate Building

- cell size 2

- min slop 0.9- min area 12

(3) Eliminate Building- cell size 2

- min slop 0.8

- min area 12

(4) Thin out

- cell size 6 3 1

- Lowest selection

(5) Filter

- Limit lower branch 3.6 - Limit Upper branchUpper haft weigh - 1.2 0.075 Upper haft weigh 0.8 0.3 0.05

Upper slant - 1.2 0.075 Upper slant 0.8 0.3 0.05

Upper tolerance - 1.2 0.15 Upper tolerance 2.4 0.9 0.10

Penetration Rate 80% 70% 70%

(6) Interpolate

- Derive point per CU to aimat 90 25 25- Grid width 6 3 1

(7) Sort Out- Eliminate building data

Upper dist 1.0 0.9 0.075

Lower dist 2.2 1.2 0.075Slop 2.0 - -

Iteration 3

Time


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The filtering result shows classification on ground surface without the objects

such as building, vegetation, and the on surface objects.

Figure 4.4 the result of filtering on ground surface from DSM to DTM

Figure 4.5 Zoom in the result of filtering on ground surface from DSM to DTM


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5 Interpolations of Ground Points

The goal of this step is fill all gaps from the result of image classification. Weare applying the filter to fill the gap from robust iteration and the parameters are showing

on Diagram 5.1. First the surface interpolation needed to be done in order to calculate the

classify point with linear prediction and define grid size to obtain on terrain detailsurface. The next step is to sort out the data by limit upper and lower surface for DTM

generation. Fill void area model are inserted on this step to detect and fill the holes by

using point around the hole to interpolation fill gap areas. Interpolation is the last processfor fill the gap, in order to calculate the DTM generation from fill hold parameter.

Diagram 5.1 fill hole of DTM generation

(6) Interpolate

- Derive point per CU to aimat 12

- Grid width 2

(7) Sort Out- Eliminate building data

Upper dist 3.0

Lower dist 3.0

Slop -

Fill void area

- resembling interval 10

- Bridging distant 200

(6) Interpolate

- Derive point per CU to aimat 16- Grid width 2


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6 Quality assessments

The Quality assessment is the process to evaluate between the product of DTM

generation and the product of DTM from LIDAR data by generating a comparisonsurface which represents the difference of two surfaces by different colors.

After DTM generation process, we got DSM in format XYZ (terrain points).The result is shown in GEOMEGIC STUDIO for wrapping surface model. Comparisonprocess staring with import DTM LIDAR and generate compare surface with the result of

DTM generation. The result shows in Figure 6.1. The result shows that there are the

existing big building and some giant vegetation are leave in the surface that shows in blueareas. On the other hand, for small building and small holes are eliminated.

Figure 6.1 Comparison DTM generation and DTM LIDAR


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As for the previous result is still have big different between two surface, The

manual elimination for buildings are provided in order to reduce the different betweensurfaces as shown in Figure 6.2. The process started with selecting the big building as

shown in dark brawn color and manual deleting all point cloud existing which are shown

the result in Figure 6.3. The interpolation on ground surface in order to produce a newDTM.

Figure 6.1 manual eliminating huge building

Figure 6.2 result of manual eliminating huge building


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The results of manual building elimination for DTM generation comparing toreference DTM LIDAR is showing in Figure 6.4. There are some differences between

those two DTM. The height difference is 10 meter. That means it can be reduced the

height difference from the full automatic fill gap DTM. In the previous results, the heightdifference was 20 meter.

The result still shows different surfaces because in the process of classificationon ground surface it cannot eliminate all slopes that connected between building and

ground surface. So the result of DTM generation is still included off surface slope. When

we use this model to generate the gap area, the fill area will create the surface cover all

the hole which includes the height. If we reduce the searching window size in order tosearch the precision resembling points cloud to fill the hole. The result of filled hole

surface shows that the big size holes cannot be filled. Only small holes are filled. In the

same time, if we increate the searching window size, the result will show that the hole

can be filled but they will include the existing slope area to generate fill area as show inFigure 6.4.

Figure 6.4 Compare manual DTM with DTM LIDAR


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On the other hand, we can test the parameter of hole filling process by applying

to the ground classification LIDAR DTM in order to compare between both results which

are same parameter. The results show that the parameter of void filling process workswell with ground classification LIDAR DTM as shown in Figure 6.5.

There are different between LIDAR DSM and DSM from aerial image in caseof building areas. LIDAR DSM has many points cloud on the top of building and only

few points in the vertical objects such as the wall. So it is easy to detect that they are

buildings and to classify them. On contrary, DSM, which produces from aerial image,

shows that there are slope between buildings to ground surface. As the result, it iscomplicated to remove all the existing slope from the model.

Figure 6.5 DTM form LIDAR data from same parameter with fill hold DTM aerial image


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After LIDAR DTM production with the same parameter from DTM aerial

image, we can check the quality of DTM with the reference DTM LIDAR and comparewith the result before. As shown in Figure 6.6, the result of DTM LIDAR comparison

with the reference DTM LIDAR is represented. The result shows there are only few

meters different in case of the huge building that means the algorithm and the parametercan work well with LIDAR data to search the whole objects and eliminate its off ground

points. Thus, the filter parameters can fill the gaps on the terrain without slope between

objects and ground surface. Finally, precision DTM can be generated.

Figure 6.6 The result of comparison between DTM LIDAR and reference DTM LIDAR

7 Conclusions

This project shows processes and results of creation DSM from aerial image and

filtering to DTM generation step by step. The image preprocessing shows how to reducethe effects of radiometric resolution and enhances the object image to perform in

matching process. As for DSM generation from aerial image, it can reconstruct all of the

objects surface which are different in height.


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DSM to DTM generation 31 5/29/2008

The project represents mainly discussion on DSM to DTM generation focusing

on the step of DTM generated from aerial image. The DTM produced by algorithm of

SCOP++ which generated points cloud of classification object and generated groundsurface DTM. The resultd of automatic DTM from aerial images need carefully provide

the filter parameters and parameters of fill hole object terrain. Manual editing are

provided for the large building because algorithm can not eliminate the existing slopebetween object and ground surface. However, in case of residential and on terrain object

(for example air crafts) can be eliminated well. As for the large vegetation, there is the

same problem with the huge building because DSM generation from aerial image will

produce a smooth slope between the objects and the ground terrain.

Nevertheless, the process and provided parameters can strongly produce rapidly

DTM from the large area and need only few of big buildings to manual elimination. The

void filling process is shown that can use searching window for detecting the holes andfilling area with spatial interpolation of the neighbor points around the holes.

8 References

Demir N., Baltsavias E., 2007. Object extraction at airport sites using DTMsDSMs and

multispectral image analysis, International Archives of the Photogrammetry, RemoteSensing and Spatial Information Sciences, Vol 36, Issue 3/W49B, on CD-ROM, Munich,

Germany

K. Kraus, C. Briese, M. Attwenger, N. Pfeifer: Quality Measurements for Digital Terrain

Models.

Karl Kraus, Johannes Otepka, 2005. DTM modeling and Visualization The SCOP

Approach. Photogrammetric Week 5, P241-251.

Kraus, K., Pfeifer, N., 1998. Determination of terrain models in wooded areas with

airborne laser scanner data. ISPRSJournal, Vol. 53.

K. Kraus, N. Pfeifer, 2001. Advance DTM Generation from Lidar Data. International

Archive of Photogrammetry and Remote Sensing, Volume XXXIV-3/4.

Gruen A., Kocaman S., Wolff K., 2007: High accuracy 3D processing of stereo satellite

images in mountainous areas. Dreilaendertagung 2007, Muttenz-Basel, Switzerland, 19-

21 June.

reduction of dsm to dtm and quality assessment

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