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GIS to DSM fusion and basic change detection. Timothée Bailloeul 11 Dec, 2003. Results over Beijing area - zoom Asian Games Stadium – Beijing (1999) Introduction

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Gis to dsm fusion and basic change detection l.jpg

GIS to DSM fusion and basic change detection.

Timothée Bailloeul11 Dec, 2003.


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Results over Beijing area - zoom

  • Asian Games Stadium – Beijing (1999)


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Introduction

  • Background : some Digital Surface Models (DSMs) have been generated from aerial images (urban environment). DSM are coarse and have pixels with no altitude info.

  • Issue : we want to merge the DSM information with the building layer of GIS data to :

    • Add a third dimension to the GIS data : 2D  3D

    • Check out if GIS-to-DSM change detection is possible

  • Key points :

    • How to merge GIS and DSM data ?

    • Quantify the change detection method


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Introduction

  • Here results towards GIS-to-DSM fusion using geocoding information will be presented.

  • Quantifying the DSMs geocoding accuracy is then essential.

  • Geocoding information will also be used to merge the generated DSMs.

  • A simple method aiming at detecting changes between GIS data (old) and the DSM (new) was carried out.


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Contents

  • DSM geocoding quality evaluation

  • DSMs merging

  • Basic change detection


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1. DSM geocoding quality evaluation

GIS TO DSM FUSION AND BASIC CHANGE DETECTION


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1. DSM geocoding quality evaluation

  • Around 10 Ground Control Points (GCPs ) are used to quantify geocoding accuracy of a DSM.

  • For each GCP we know:

    • Its coordinates in the Beijing System (East,North,Altitude)

    • Its image coordinates (row,col) in the non-orthorectified DSM.

  • For each GCP :

    • We collect the (E,N,A) information from the non-ortho DSM at the GCPs (row,col) location, and compute the difference.




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1. DSM geocoding quality evaluation

  • Results : geocoding precision is ranging from 0.5 to 1 meter

  • Good enough for GIS-to-DSM registration (1-1.5 pixel)

  • DSMs merging using geocoding information can be tested


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1. DSM geocoding quality evaluation

  • GIS-to-DSM registration

    • GIS and DSM data are both projected in the same cartographic system.

    • GIS building layer is a list of simple polygons (convex or concave)

      • Each polygon is a sub-list of vertexes which coordinates are in the Beijing system (cyclic list)

      • Each polygon is projected on the DSM by (E,N,A)  (row,col) conversion.


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1. DSM geocoding quality evaluation

  • GIS-to-DSM

    registration

    -- results


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1. DSM geocoding quality evaluation

  • GIS-to-DSM

    Registration

    -- results


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1. DSM geocoding quality evaluation

  • GIS-to-DSM

    Registration

    -- results


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2. DSM merging

GIS TO DSM FUSION AND BASIC CHANGE DETECTION


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2. DSM merging

  • Merging n DSMs is not so easy :

    • Within the overlapping area, what value to choose among n ?

    • Shall we take the mean, median,…?

    • How to manage outliers ?

  • Solution :

    • Take into account the inter-consistency of the DSMs, i.e. the range of the DSMs altitude values

    • Take into account the intra-consistency of the merged DSM, i.e. the coherence of each DSM value versus the neighborood of the already fused pixels.


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2. DSM merging

  • Few definitions for n=2 :

    • Inter-consistency of 2 DSMs.

      • DSM1i,j and DSM2k,l are consistent if

    • Intra-consistency of the merged DSM and DSM1.

      • DSM1i,j is consistent with the fused DSM neighborhood if

      • Where Neigh_DSMo,p is the mean computed over a 5*5 causal window in the already merged DSM.


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2. DSM merging

  • Algorithm for n=2 and within the overlapping area :

    • Build the geocoded bounding box

    • For each pixel of that grid :

      • Check if both DSM have altitude info

        • If none has altitude info, REJECT

        • If only one has, ASSIGN the value

        • If both have

          • If the points are inter-consistent  take the mean altitude of the points that are intra-consistent

          • Else take the value of the most intra-consistent point


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2. DSM merging

  • Result :


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Results over Beijing area - zoom

  • Results : Olympic village area


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Results over Beijing area - zoom

  • Results : Olympic village area

Merged DSM over overlapping area using the presented method

Merged DSM over overlapping area using the average strategy only


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3. Basic change detection

GIS TO DSM FUSION AND BASIC CHANGE DETECTION


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3. Basic change detection

  • Problem statement :

    • GIS and DSM data can be easily registered using their geocoding information

    • Transfer altitude to GIS layer

      • DSM is coarse, so transferred altitude information is also coarse (median)

    • Basic change detection (CD) is desirable

      • Gives an a priori to the later GIS-to-satellite image CD

      • DSM is coarse, so the method must be simple and will provide limited results


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3. Basic change detection

  • What is a change ?

    • In the real world :


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3. Basic change detection

  • What is a change ?

    • From map inconsistencies :

      • Mistakes from photo-interpretors who made the GIS

      • Unaccuratly extracted buildings outlines (revisioning)


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3. Basic change detection

  • What is a non-change ?

    • In the real world :

      • Isolated unchanged building with flat roof

      • Isolated unchanged building with non-flat roof

      • Not isolated unchanged building


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3. Basic change detection

  • What can we do with the DSM ?

    • Design a simple CD method :

      • Using statistical global criteria (mean, median, variance) towards altitude info from DSM.

      • Robust to the GIS and GIS-DSM matching inaccuracies.

    • Limitations :

      • DSM is coarse, change detection may be limited to the most simple and obvious CD cases.

      • The implemented method can handle cases : a,c*,i


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3. Basic change detection

  • CD method

    • For each polygon registered to the DSM, do :

      • If percentage of pixels with altitude info within the polygon OR in its neighborhood is < det_thres, then IMPOSSIBLE TO STATE

      • Elseif the polygon and its neighborhood altitude is flat (i.e. their standard deviation are < flat_thres) then :

        • If the altitude difference between the polygon and its neighborhood is > build_height_thres, then UNCHANGED

        • Else CHANGED

      • Else the building is classified as AMBIGUOUS


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3. Basic change detection

  • Results with :

  • det_thres = 10%

  • flat_thres = 1m

  • build_height_thres = 3m

  • GIS data : 1996

  • DSM : 1999


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3. Basic change detection

Flat_thres = 1m

Build_height_thres = 3m


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3. Basic change detection

Det_thres = 50%

Build_height_thres = 3m


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3. Basic change detection

Det_thres = 50%

Flat_thres =1.5 m


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3. Basic change detection

  • Comments towards the results :

    • Few buildings are classified UNCHANGED or CHANGED (consistent to the initial hypothesis).

    • The more selective the parameters, the better the success rate.

    • The flatness parameter value is critical since the success rate is the most sensitive to it.

    • Few buildings were reported as unchanged

    • It would make more sense to compare the number of buildings detected as CHANGED to the ones that have really changed in the reality (need for ground truth).


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3. Basic change detection

  • Comments towards the results :

    • The results depend on :

      • The DSM quality (% pix with altitude info)

      • The GIS quality

      • The GIS-to-DSM matching accuracy

      • The scene :very dense urban areas won’t yield lot of results


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3. Basic change detection

  • How to improve the results :

    • Normalize with a Digital Terrain Model (DTM)

      • nDSM=DSM-DTM

        • So we get rid of the terrain altitude variation

        • Possibility to use some absolute altitude threshold, then no need to compute the neighborhood altitude

    • Make some experiments with GIS of 2001 to validate the method.

    • « Hysteresis » to get more results :

      • Start from selective to loosely parameters to have different level of confidence in the results.


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CONCLUSION

GIS TO DSM FUSION AND BASIC CHANGE DETECTION


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Conclusion

  • DSMs geocoding accuracy quantification has been presented

  • DSM geocoding is accurate and validate the camera parameters optimization process.

  • GIS-to-DSM and DSMs merging techniques have been also shown.

  • A basic GIS-to-DSM change detection was introduced

    • The number of processed buildings is limited

    • It is parameter dependent (3). Selective parameters yield best results.

    • Further improvement are possible

    • Can provide additional prior info to GIS-to-satellite image CD





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