Automated Procedures for Creating and Updating Digital City Models Using Very High Resolution (VHR) Satellite Imagery

Transcription

1 Automated Procedures for Creating and Updating Digital City Models Using Very High Resolution (VHR) Satellite Imagery Presentation for the Remote Sensing Workshop in Athens Organized by the Greek Technical and Geoachnical Chambers ( and ) Dr. George Vozikis Prof. Dr. Josef Jansa

2 Outline Introduction What are we talking about? Data Preparation ndsm Creation Workflow (Detection, Extracting Buildings) Alternative Methods Change Detection und Updating Results (Problems of the Presented Methods) Outlook, Future Work Conclusion

3 Line Scanner Imagery ADS40 IKONOS QUICKBIRD HRSC 0.61m-2.5m >0.1m Automated Processing DCM

4 Digital Surface Models Images (Orthophotos) Digital Surface Models (DSMs) San Diego: 20cm (ISTAR - ADS40) Nimes: 32cm (DLR - ADS40) Vienna: 50cm (Airborne Laser) Athens: 1m (IKONOS) Melbourne: 1m (IKONOS) Bern: 72cm (DLR - HRSCAX) Laser HRSC

5 Workflow

6 ndsm Creation Derive the DTM and ndsm automatically from the DSM. ndsm DTM DSM

7 Finding Candidates (1) Masking of Area - Seed point determination Potential Building Regions or not? Simple Height-Thresholding not sufficient 1. Derivative of DSM not target-oriented IMAGE ndsm

8 Finding Candidates (2) Checking in neighbourhood regions in the image for homogeneity by looking at statistical properties (Variance, Min- Max-Difference, Absolute Mean Deviation...) ndsm Height Threshold Areas of Interest Image Homogeneity

9 Finding Candidates (3) ADS40 subset

10 Detecting Candidates HRSC subset

11 Object Extraction (1) Adaptive Region Growing As long as the difference of geometric properties of regions of two iteration steps don t exceed a certain limit, continue with region growing with the next threshold value Open-Close (Blow-Shrink) Vectorisation (Contour Determination) * Edge extraction- and -intersection (Hough T.)

12 Object Extraction (2) Hough Transformation of a line y b y = mx+b x b = y-mx m

13 Object Extraction (3) H Hough Transformation ( θ, ρ) F( x, y) δ ρ xcos( θ ) ysin( θ ) = ( )dxdy δ: Dirac delta-function With F(x, y), every point (x, y) of the original image F is transformed into a sinusoid ρ = xcosθ -ysinθ, ρ: perpendicular distance from the origin to a line under angle θ Points lying on the same line in image space generate sinusoids in Hough Domain that intersect in one point. Inverse Hough Transformation (Backtransformation): Each point in Hough Domain corresponds to one line in image space. DEMO

14 Hough Transformation, Explanation

15 Building Outline Determination (1) Image Domain Hough Domain Image Domain

16 Building Outline Determination (2) Hough Domain Grown Region Vectorised Region Extracted Building Find Corners Backtransformation

17 Workflow - Example

18 Strengths of the Hough Transform (1) Noisy Data:

19 Strengths of the Hough Transform (2) Level of Detail:

20 Strengths of the Hough Transform (3) Forcing Angles π/2

21 Strengths of the Hough Transform (4) Bridging Gaps

22 Alternative Approaches (1) Matching

23 Alternative Approaches (2)

24 Alternative Approaches (3) Texture Analysis Occurrence: use statistical moments of grey level histograms Co-occurrence measures: relative position of the pixels with respect to each other

25 Alternative Approaches (4)

26 Alternative Approaches (5) Textural Measures:

27 Alternative Approaches (6)

28 Change Detection und Updating Find new building Find vanished buildings Compare new and old state Compute residuals Calculate rotation & translation & scaling

29 Results (1) ndsm with draped Orthophoto DCM with draped Orthophoto

30 Quantitative Results CFB: Correctly Found Buildings, NFB: Not Found Buildings (also insufficiently mapped buildings) WFB: Wrongly Found Buildings

31 Quantitative & Qualitative Results scale A: 1:1000-1:4000 scale B: 1:4000-1:12000 scale C: < 1:12000 In image space units (pixels)

32 Results Problems (1) Heterogeneous Roofs

33 Results Problems (2) Shadows HRSC San Diego

34 Results Problems (3) Complex Buildings

35 Results Problems (3) DSM Inaccuracies:

36 Results Problems (5) Compound Buildings

37 Results Problems (6)

38 Future Work Extraction of objects with holes (e.g. houses with inner courtyards), where not only the building outline, but also the hole outline are derived. Research on constraint settings for aggregating neighbouring roof parts that belong to one building. Extract edges on sub-pixel bases in order to increase the qualitative results. Integrate a hierarchical approach for decreasing computation time of image matching.

39 Conclusions (1) Method for DCM change detection and updating Limited input data Only (stereo-) panchromatic imagery Sensitive method regarding radiometry Image matching: Good results achieved for small scales. Texture analysis: Not suitable! Hough Transformation: Powerful tool! Generalization also for other purposes:

40 Conclusions (2)

41 Conclusions (3)

42 Automated Procedures for Creating and Updating Digital City Models Using Very High Resolution (VHR) Satellite Imagery