Urban Damage Detection in High Resolution Amplitude SAR Images

Transcription

1 Urban Damage Detection in High Resolution Amplitude SAR Images P.T.B. Brett Supervisors: Dr. R. Guida, Prof. Sir M. Sweeting 21st June

2 Overview 1 Aims, motivation & scope 2 Urban SAR background 3 Principal contributions 1 Fast ridge detection 2 Ridge classification by points 3 Statistical models for building shape 4 Ridge classification by shape 5 Integrated damage detection tool 4 Publications 5 Conclusions 2

3 Aims, motivation & scope Motivation In recent years, earthquakes have caused hundreds of billions of pounds of damage to urban areas and extensive loss of life. Satellite synthetic aperture radar (SAR) is an excellent source of timely, disruption-free surveillance data. Latest very-high-resolution platforms provide unprecedented levels of detail in urban areas. Fast, automated urban SAR image interpretation could support quicker disaster response, and save lives. By mid-2009, very little research done into damage detection at level of city blocks or even individual buildings. Earthquake in L Aquila, Italy in April 2009 kick-started research. 3

4 Aims, motivation & scope Aims Scope Design, implement and test an automated method for urban change detection using metre-resolution SAR amplitude images. Focus on amplitude images, due to existing SSC expertise. Use single post-event image advantages for prompt response. Coregistration techniques not in scope: already wide variety of mature approaches. Building detection identified as important related capability. Choose fast, scalable algorithms for fast results. 4

5 Urban building scene ĵ î φ b a ê B ê A Can model a building as a smooth-sided rectangular box on rough, level ground. Established model (e.g. Franceschetti et al. 2002). Height h, wall lengths a and b. θ ˆk î ê L h How does it appear in a SAR image? Which aspects of appearance are best for recognising buildings? 5

6 Scattering mechanisms Single Double Triple Shadow 6

7 Scattering mechanisms θ ê L ˆk h î Terrain backscattering Roof backscattering Triple reflection d Near wall backscattering Double reflection Shadow 7

8 Double reflection lines ê L β α Return pattern elements shadow layover double reflection (DR) line Azimuth Range DR line advantages Shape from building footprint. Brightness from building height. Both previously demonstrated. DR lines are key to detecting buildings, and changes to them. 8

9 Ridge Detection Methods In the SAR literature Hough transform Classical approach (Wood 1985). Very inefficient for shapes other than circles/straight lines. Curvelet transform Fast for detecting areas containing ridge-like structures. Not capable of pixel/sub-pixel precision. Steger operator Used widely in most recent SAR literature. Cost of high extra precision wasted on very narrow lines. Lindeberg ridge detector Not previously used in SAR literature. Compromise between precision and speed. Opportunity to test technique new to the field w.r.t. speed and precision. 9

10 Ridge detection toolkit Contribution: New, open source implementation of Lindeberg ridge detector for SAR applications. Highly scalable to large images: O(N) in number of pixels. Scalable to multiprocessing: O(1/k) in process count. GPL-licensed source code available for Linux. Ridge file format and IO library to enable further varied research projects using ridge data. 10

11 Ridge detection toolkit Input image Scale-space generation Metric generation Ridge point detection Line construction Ridge lines Ridge points/segments 11

12 Ridge point definition q p r 0 y f (x, y) x 12

13 Ridge point definition Use the image s Hessian matrix to find the (p, q) directions at each point: Hess (f (r 0 )) = 2 f (r 0 ) 2 f (r 0 ) x 2 yx 2 f (r 0 ) 2 f (r 0 ) xy y 2 This is real & symmetric, so it can be diagonalised: Hess (f (r 0 )) = [ ] [ ] T kp 0 [ ] ν p ν q νp ν 0 k q q 13

14 Ridge point definition A point r 0 is a ridge point if and only if: ν T p f (r 0 ) = 0 k p < 0 k p > kq 14

15 Ridge line construction Naïve algorithm Find start of each line and walk along it to find all connected points. Cannot be divided between multiple processes! Novel algorithm Note that each ridge point can only be in a single line. Each line is a set of ridge points, and the sets are disjoint. Use wait-free disjoint set algorithm (Anderson & Woll 1991). Permits very efficient use of multiprocessing. 15

16 Ridge extraction results Azimuth Range Ridge points 16

17 Ridge extraction results Azimuth Range Ridge lines 17

18 Ridge extraction results Azimuth Range 18

19 Ridge point statistics Contribution: Ridge classification using properties of ridge points. Proposed two feature variables for classification: 1 image brightness at each point X 1 2 ridge strength (saliency) at each point X 2. Two classes: 1 DR lines B P 2 other features B P. Estimated maximum likelihood distributions for each variable. Used G 0 A model (Frery et al. 1997). 19

20 Point brightness distribution Probability density P ( X 1 B P ) P (X 1 B P ) Relative scattering amplitude 20

21 Point strength distribution Probability density P ( X 2 B P ) P (X 2 B P ) Relative saliency 21

22 Classification of ridge points Used a naïve Bayesian classifier with decision rule: ln p (B P) p ( B P ) + 2 ln p (x i (r) B P p ( ) BP x i (r) B P i=1 B P 0 22

23 Ridge point classification results Azimuth Range Reasonable accuracy of 84%, but very high miss rate (48%). 23

24 Classification of ridge lines Two classes B L and B L. Proposed new Bayesian decision rule to combine posterior likelihood of all points: ln p (B L) p ( B L ) + N j=0 2 i=1 ln p ( ( ) ) x i rj BL p ( ( ) ) BL 0. x i rj BL B L 24

25 Ridge line classification results Azimuth Range Higher accuracy (87%), lower miss rate (23%), more relevant results! 25

26 Point-based classification performance 1 Detection rate ptp Lines Points False alarm rate p FP 26

27 Statistical models for building shape ê B ĵ φ b î a ê A Interesting facts: t = ab is independent of φ θ l = a + b is independent of φ ê L ˆk h î 27

28 Statistical models for building shape Contribution: Novel statistical models for shape of buildings double reflection lines. It can be shown that: t = αβ ab ê L β α If slant range resolution is same as azimuth resolution, then: l = α + β a + b Two models proposed using these relationships: Gamma Area (GA) Gamma Area-Length (GA). 28

29 Modelling building sizes Problem: how to model prior p.d.f. of building dimensions (A, B) = (a, b)? Literature search unsuccessful! Assumed i.i.d. and Gamma model A Γ(k, m). Single-sided p.d.f. with several common distributions as particular cases. Priority area for further investigation. 29

30 Gamma Area model The p.d.f. for t = αβ was shown to be: f T (t) = 2 ( tγ (k) This is a product p.d.f. t c t m 2 ) k ( K 0 2 t c t m 2 It is a form of the K-distribution previously used to model speckle (Redding 1999). c t is a constant that accounts for image resolution. ). 30

31 Gamma Area Length model The joint p.d.f. for (t, l) = (αβ, α + β) was found by change of variables using the Jacobian: 2 ( t ) k 1 ( f T,L (t, l) = Γ 2 (k) m 2k λ exp l 2 4t λ 2 l ) mλ This was possible due to the bijective & differentiable mapping. λ is the slant range and azimuth resolution of the image. 31

32 Classification by building shape Defined a likelihood threshold decision rule for each feature C: log p ( C ϑ ) B L B L τ p(c) evaluated using GA or GAL model ϑ are model parameters τ is a likelihood threshold. 32

33 Shape-based classification results Azimuth Range GA model, A Γ(4, 8) 33

34 Shape-based classification results 1 Detection rate ptp GA model GAL model Point-based False alarm rate p FP 34

35 Damage detection framework Contribution: Fast, mostly-unsupervised urban earthquake damage detection tool. Advantages Highly scalable: computational complexity and resource requirements O(N) throughout; fully parallel. No supervised steps if appropriate building size priors assumed. Limitations Change detection metric (Guida et al. 2010) detects changes even in very small features. May pick up specular lines as well as DR lines. Closely-spaced buildings/vegetation may mask damage. 35

36 Damage detection framework Pre-event image Post-event image Ridge detection Coregistration Feature selection Ratio Mask EM model Damage estimation Damage map 36

37 Damage estimation rule Quotient 0 < d < 1 introduced by Guida et al. in 2010: d = h 1 h 2 = h 1 c ˆσ 0 2 r 1 In this framework, estimated from ratio image using: R 2 d (r) = 1 R 2 (r) ˆσ

38 Case studies Port-au-Prince, Haiti 12th January :53 UTC COSMO-SkyMed image pair, look angle 38 L Aquila, Italy 6th April :32 UTC COSMO-SkyMed image pair, look angle 20 38

39 Haiti: Cathedral 39

40 Haiti: Cathedral 40

41 Haiti: National Palace 41

42 Haiti: National Palace 42

43 L Aquila: Via Amiternum area 43

44 L Aquila: Via Amiternum area Infill damage 44

45 L Aquila: central square Cathedral San Massimo 45

46 L Aquila: central square 46

47 Damage detection results Advantages Successfully detected roof, wall and soft storey collapse. Flexible enough to cope with non-ideal scenes. Drawbacks Fails when no suitable bright line is found. Especially dense cities e.g. old town areas. False positives due to speckle noise. Does not account for hills/valleys (georeferencing). 47

48 Future work Multi-scale ridge extraction; add Windows support to tools. Test classifiers with other feature extractors (e.g. Steger). Improve building shape models using GIS data. Combine shape- and point-based classification approaches. Quantify damage detection performance using ground truth. Reduce false positives due to speckle. Fuse with other change detection approaches (Dell Acqua 2012, Bovolo 2012). 48

49 Publications Journal papers Brett, P.T.B. and Guida, R. Earthquake Damage Detection in Urban Areas using Curvilinear Features. IEEE Trans. Geosci. Remote Sens (forthcoming). Brett, P.T.B. SSC Ridge Tools: curvilinear feature detection for remote sensing. J. Open Research Software (submitted). 49

50 Publications Conference papers Brett, P.T.B. and Guida, R. Bright line detection in COSMO-SkyMed SAR images of urban areas. JURSE 2011, Munich. Guida, R. and Brett, PTB. A SAR image-based tool for prompt and effective earthquake response. JURSE 2011, Munich. Brett, P.T.B. and Guida, R. Geometry-based SAR curvilinear feature selection for damage detection. EUSAR 2012, Nuremberg. 50

51 Publications Software packages SSC Ridge Tools (ssc-ridge-tools) SSC Ridge Classifiers (ssc-ridge-classifiers) SSC Urban Change Detection Tools (ssc-urban-change) All open source and available at 51

52 Conclusions Successfully exploited new VHR satellite SAR data. Proposed a novel approach to locating building DR lines. Introduced novel, unsupervised change detection framework. Demonstrated capability using real-world data. Identified potential for future investigation. Effective dissemination with high reproducibility/reusability. 52

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