1. Geometric Activity Indices for Classification of Urban man-made Objects using Very-High Resolution Imagery R. Bellens, L. Martinez-Fonte, S. Gautama Ghent University, Belgium

2. Geometric Activity IndicesIntroduction 2/23 Experimental Results Outline 1.Introduction Use of Spatial Information for Land-Use Classification Per Pixel Geometric Activity Indices –Evaluation 2.Geometric Activity Indices Raw indices –Ridge-based –Morphological Summarized Indices Object-based Indices for Evaluation 3.Experimental results Settings and Classes Definition Results and Discussion

3. Geometric Activity IndicesIntroduction 3/23 Experimental Results Outline 1.Introduction Use of Spatial Information for Land-Use Classification Per Pixel Geometric Activity Indices –Evaluation 2.Geometric Activity Indices Raw indices –Ridge-based –Morphological Summarized Indices Object-based Indices for Evaluation 3.Experimental results Settings and Classes Definition Results and Discussion Introduction

4. Geometric Activity IndicesIntroduction 4/23 Experimental Results Use of Spatial Information for Land-Use Classification Classification Introduction

5. Geometric Activity IndicesIntroduction 5/23 Experimental Results Use of Spatial Information for Land-Use Classification Classification Introduction Morphological profile (Benediktsson) Shape Descriptors from Angular Texture Signature (Couloigner) Edge Maps (Haverkamp) Corner Points and Edges (Phalke & Couloigner)

6. Geometric Activity IndicesIntroduction 6/23 Experimental Results Classification Introduction Per Pixel Classification 1. Detection of image features (e.g. lines) 2.Generate per-pixel activity indices (e.g. indication of the presence of a line) 3.Each index is an input layer to the classification, together with spectral bands. Hipotesis: Man-made objects usually exhibit nice geometric features, then Geometric Activity can indicate a specific type of man-made object.

7. Geometric Activity IndicesIntroduction 7/23 Experimental Results Geometric Activity + Spectral bands Introduction Per Pixel Classification - Evaluation Object-based features (e-cognition) + Spectral bands Classification

8. Geometric Activity IndicesIntroduction 8/23 Experimental Results Outline 1.Introduction Use of Spatial Information for Land-Use Classification Per Pixel Geometric Activity Indices –Evaluation 2.Geometric Activity Indices Raw indices –Ridge-based –Morphological Summarized Indices Object-based Indices for Evaluation 3.Experimental results Settings and Classes Definition Results and Discussion Geometric Activity Indices

9. Introduction 9/23 Experimental Results Geometric Activity Indices 1. Raw indices scale-space signatures  each scale = 1 image layer –Ridge-based –Morphological 2. Summarized Indices 3. Object-based Indices for Evaluation Geometric Activity Indices scale Index value Per-pixel signature 1 3 5 7 9 11 13 99 0 0 scale 1 scale 3 scale 5 scale 7 scale 9 …

10. Geometric Activity IndicesIntroduction 10/23 Experimental Results Raw Ridge-based Indices Gradient and Eigenvalues of the Hessian matrix (Steger98) ||G|| 1 2 class 000flat 0--peak 0++valley 0-0ridge 0+0valley +00slope +-- +++ Geometric Activity Indices

11. Introduction 11/23 Experimental Results Raw Ridge-based Indices Geometric Activity Indices Original image Gradient MinEigenvalue Window size = 11 MaxEigenvalue

12. Geometric Activity IndicesIntroduction 12/23 Experimental Results Raw Morphology-based Indices Closing with Disk 04812 Geometric Activity Indices Open/Closing with circular structuring element Open/Closing with linear structuring element

13. Geometric Activity IndicesIntroduction 13/23 Experimental Results Raw Morphology-based Indices Closing with Lines 081624 Geometric Activity Indices Open/Closing with circular structuring element Open/Closing with linear structuring element

14. Geometric Activity IndicesIntroduction 14/23 Experimental Results Summarized Indices Software tool interface for the Multiple Discriminant Analysis and generation of summary geometry activities features Geometric Activity Indices Multiple Discriminant Analysis (MDA)

15. Geometric Activity IndicesIntroduction 15/23 Experimental Results # of features Based on 4 Maxeigen, Mineigen and Gradient signatures 4 Closing signatures with disk 2 Opening signatures with disk 3 Open/Close with linear structuring elements Summarized Indices Geometric Activity Indices Sample feature 2 Indication of small dark structures (based on Closings) Sample feature 1 Indication of large roads (based on Ridge)

16. Geometric Activity IndicesIntroduction 16/23 Experimental Results Object-based Indices used for Evaluation (6/24) Geometric Activity Indices (Calculated with e-cognition)

17. Geometric Activity IndicesIntroduction 17/23 Experimental Results Outline 1.Introduction Use of Spatial Information for Land-Use Classification Per Pixel Geometric Activity Indices –Evaluation 2.Geometric Activity Indices Raw indices –Ridge-based –Morphological Summarized Indices Object-based Indices for Evaluation 3.Experimental results Settings and Classes Definition Results and Discussion Experimental Results

18. Geometric Activity IndicesIntroduction 18/23 Experimental Results QuickBird panchromatic: 60 cm multispectral: 2.4 m blue green red infra-red Experimental Results

19. Geometric Activity IndicesIntroduction 19/23 Experimental Results Classes Definition Experimental Results

20. Geometric Activity IndicesIntroduction 20/23 Experimental Results Classification Scenarios  Only Spectral information (4xs + pan) and NDVI 6 layers  Geometric Activities (GA) indices +13 features  Object-based features (eCognition®) + 24 features  Combined GA and Object-based indices + 37 features Experimental Results

21. Geometric Activity IndicesIntroduction 21/23 Experimental Results Effect of Adding Geometric Activity Indices Experimental Results Spectral features only Spectral + GA features

22. Geometric Activity IndicesIntroduction 22/23 Experimental Results Geometric Activity vs Object-based Indices Experimental Results Spectral + GA features Spectral + Object-based features

23. Geometric Activity IndicesIntroduction 23/23 Experimental Results Man-Made Objects Classification Accuracy Experimental Results

24. Geometric Activity IndicesIntroduction 24/23 Experimental Results Conclusions (1 / 2) Ridge features: Per-pixel indication of the presence of a linear structure Problem: unreliability near borders (good to locate roads, not for delineation of roads) little improvement in distinguishing between the road class and the roof class Improves distinction of the classes water and shadow (geometrically these classes are very different: the class water consists of mainly large objects, which results in very low responses on the lower scales, while the class shadow consists of small objects, which results in high responses on the lower scales) Since the water objects are very large, only a small part of it is affected by border effects

25. Geometric Activity IndicesIntroduction 25/23 Experimental Results Conclusions (2 / 2) Morphological features Disk shaped structuring elements indication of the minimum size of objects Derived features seemed especially useful for detecting isolated houses Linear structuring elements indication of the maximum size of objects Useful to detect linear objects which have large maximum and small minimum sizes Together, these features gave significant improvements on the accuracies of the class roof and especially of the class road.

26. Geometric Activity Indices for Classification of Urban man-made Objects using Very-High Resolution Imagery R. Bellens, L. Martinez-Fonte, S. Gautama Ghent University, Belgium