Transcript Shadow change detection

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. China
Email: [email protected]
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Outline
• Introduction
• Methods
• Results and Discussion
• Conclusion
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Introduction
Prompt 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 assessment
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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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Multilevel segmentation method
(Multichannel watershed transformation + dynamics of
contours)
Multispectral image
Multispectral gradient
Initial segmentation result by
watershed transform
Dynamics of watershed
contours
Hierarchical segmentation
results
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.
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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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One-class Support Vector Machine
(OCSVM)
Hyperplane of separation
+1
+1: target class
-1
-1: outlier
w
Target
samples
classified as outliers
Origin
• 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
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Shadow change detection
1, Shadow detection from bi-temporal images
A histogram thresholding method for shadow detection
Based on intensity difference of shadow and non shadow areas
Bimodal 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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Results
Study area: Dujianyan, China
Datasets: Quickbird images
(2005, 2008)
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Initial building damage detection result
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Spectral data
only
Shadow change information
Black: shadow change
White: no shadow
change
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Result comparison
Building damage detection results by different methods (all in %)
Damaged
PA
UA
Spectral
only
Proposed
method
Undamaged
PA
UA
OA
Kappa
69.63
66.41
84.82
86.63
80.25
53.71
63.73
84.75
95.06
84.44
85.88
63.25
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Result comparison
Spectral only
Proposed method
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Result comparison
Before
After
Spectral only
No damage
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Damage
Proposed method
Result comparison
No damage
Before
After
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Spectral only
Proposed method
Damage
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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