Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information

Peijun Li, Benqin Song and Haiqing Xu

Peking University, P. R. ChinaEmail: pjli@pku.edu.cn

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Outline

• Introduction

• Methods

• Results and Discussion

• Conclusion

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IntroductionPrompt and accurate detection of damage to urban infrastructure caused by disasters (e.g. earthquakes)

Very high resolution satellite (VHR) images

Automated detection and assessment methods: urgently required

Fusion of different sensor data, use of single source data

Existing methods (VHR optical data): mostly spectral data only,

Objective: use of shadow change information to refine results

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Methods

• Image segmentation

• Initial building damage detection and shadow change detection

• Result refinement using shadow information

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Flowchart of method

Bitemporal images

Bitemporal image segmentation

Initial building damage detection: OCSVM

Shadow and its change detection

Result refinement

Final result

Accuracy assessment5

Image segmentation

• Image segmentation on bitemporal images, in order to keep consistent object boundary

• A multilevel hierarchical segmentation method required:

initial building damage detection, shadow identification and change detection: different segmentation levels

Multitemporal segmentation

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Multispectral image

Multispectral gradient

Initial segmentation result by watershed transform

Dynamics of watershed contours

Hierarchical segmentation results

Multilevel segmentation method(Multichannel watershed transformation + dynamics of contours)

Li, P., Guo, J., Song, B. and Xiao, X., 2011, A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 103-116.

Initial building damage detection using OCSVM

Building damage (‘building to non-building’): target class

Multi-date composite classification: One-class Support Vector Machine (OCSVM) – one-class classifier

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w

Origin

Hyperplane of separation

Target samples classified as outliers

+1

-1

One-class Support Vector Machine (OCSVM)

• Only samples of target class (e.g. building damage) required in training process

• find the maximal margin hyperplane, which best separates the training data from the origin: more training samples, less outliers

+1: target class

-1: outlier

Shadow change detection

1, Shadow detection from bi-temporal images

A histogram thresholding method for shadow detectionBased on intensity difference of shadow and non shadow areasBimodal histogram: shadows occupying the lower end of the histogram

2, Shadow change detection: comparison of shadows detected from two-date images

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Result refinement using shadow change information

• If a building collapsed, the shadow will disappear.

• After building collapse and shadow change were detected, a simple conditional statements to refine the result:

For each building collapse area detected, if it is adjacent to an area with shadow change, then it will be remained. Otherwise, it will be considered as non building damage area and will be removed.

• The detected patches less than the size of the average buildings in the scene were removed by thresholding.

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Study area: Dujianyan, China

Datasets: Quickbird images (2005, 2008)

Results

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Initial building damage detection result

Spectral data only13

Shadow change information

Black: shadow changeWhite: no shadow change

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Result comparison

  Damaged Undamaged OA Kappa  PA UA PA UA    

Spectral only 69.63 66.41 84.82 86.63 80.25 53.71

Proposed method 63.73 84.75 95.06 84.44 85.88 63.25

Building damage detection results by different methods (all in %)

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Result comparison

Spectral only Proposed method

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Before After Spectral only Proposed method

No damage

Damage

Result comparison

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No damage

DamageBefore After

Spectral only Proposed method

Result comparison

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Conclusion

Combination of spectral information and shadow change information produced significantly higher accuracy than the use of spectral information alone.

Further investigation: * how to extract shadow more accurately, * dealing with partly damaged buildings (some walls still intact), * more datasets to evaluate,..

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Thank you for your attention!

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