Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow...
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![Page 1: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/1.jpg)
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: [email protected]
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![Page 2: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/2.jpg)
Outline
• Introduction
• Methods
• Results and Discussion
• Conclusion
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![Page 3: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/3.jpg)
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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![Page 4: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/4.jpg)
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
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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.
![Page 8: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/8.jpg)
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
![Page 10: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/10.jpg)
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
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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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![Page 18: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/18.jpg)
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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![Page 20: Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649efd5503460f94c10b9a/html5/thumbnails/20.jpg)
Thank you for your attention!
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