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Using Wavelets as an Effective Using Wavelets as an Effective Alternative Tool for Wind Disaster Alternative Tool for Wind Disaster Detection from Satellite Images Detection from Satellite Images Sudha Radhika, Sudha Radhika, Yukio Tamura, Masahiro Yukio Tamura, Masahiro Matsui Matsui Contact: [email protected] Contact: [email protected] kougei.ac.jp Wind Engineering Research Center Wind Engineering Research Center Tokyo Polytechnic University Tokyo Polytechnic University Japan Japan

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Using Wavelets as an Effective Using Wavelets as an Effective Alternative Tool for Wind Disaster Alternative Tool for Wind Disaster

Detection from Satellite ImagesDetection from Satellite ImagesSudha Radhika, Sudha Radhika,

Yukio Tamura, Masahiro Yukio Tamura, Masahiro MatsuiMatsuiContact: [email protected]: [email protected]

Wind Engineering Research CenterWind Engineering Research CenterTokyo Polytechnic UniversityTokyo Polytechnic University

Japan Japan

OBJECTIVEOBJECTIVE

Automated IdentificationAutomated Identification of of Wind Wind Damaged Building StructuresDamaged Building Structures from from the Prethe Pre-- and Postand Post-- Satellite Images Satellite Images usingusing WaveletWavelet as an effective toolas an effective tool

PAST RESEARCHES PAST RESEARCHES •• Major contribution in disaster Major contribution in disaster

detection in detection in earthquakeearthquake using using aerial imagesaerial images by Hasegawa et al by Hasegawa et al 2000, Mitomi et al 2001, Sumer et 2000, Mitomi et al 2001, Sumer et al 2004 and Ozisik 2004al 2004 and Ozisik 2004

•• Major contribution using Major contribution using satellite satellite imageryimagery is done by Matsuoka et al is done by Matsuoka et al 2000, Vu et al 2005, in 2000, Vu et al 2005, in earthquake earthquake disaster. disaster.

PAST RESEARCHES PAST RESEARCHES •• Researches were also done by Researches were also done by

computational identification using computational identification using low resolution satellite imageslow resolution satellite images in in other natural disasters like other natural disasters like (1)(1) Wild fire by Ambrosia et al 1998,Wild fire by Ambrosia et al 1998,(2)(2) Flood by Groeve et al 2009, Flood by Groeve et al 2009, (3)(3) Landslides by Danneels et al 2008 Landslides by Danneels et al 2008

and so on.and so on.

PAST RESEARCHES PAST RESEARCHES •• In In windwind disaster major contribution is disaster major contribution is

done by done by Womble et al 2007 and Womble et al 2007 and Womble 2005Womble 2005, using the ordinary , using the ordinary statistical analysis of the histogram of statistical analysis of the histogram of the the high resolution satellite imagehigh resolution satellite imagepixel radiance value. pixel radiance value.

•• Introduction of Introduction of RSRS--ScaleScale (Remote (Remote Sensing Scale) table rating building Sensing Scale) table rating building damage scale by Womble 2005. damage scale by Womble 2005.

PAST RESEARCHES PAST RESEARCHES More accurate and faster the damage More accurate and faster the damage identification identification save more lives and more building save more lives and more building

structures can be restored faster.structures can be restored faster.

WaveletsWavelets + + ANNANN + High resolution + High resolution satellite imagerysatellite imagery

RS SCALERS SCALERemote Sensing

ScaleGround Truth Data Visual Inspection of Satellite Images

RS1 No apparent damage•No significant change in texture, color, or edges.• Edges are well-defined and linear.• Roof texture is uniform.• Larger area of roof may be visible.

RS2 Shingles/tiles removed, leaving decking exposed

•Nonlinear, internal edges appear • Newly visible material gives strong spectral return.• Original outside roof edges are still intact.

RS3Decking removed, leaving roof structure exposed

•Nonlinear, internal edges appear • Holes in roof may not give strong spectral return.• Original outside edges usually intact.

RS4Roof structure collapsed or removed. Walls may have collapsed.

•Original roof edges are not intact.• Texture and uniformity may or may not experience significant changes.

OUTPUTOUTPUTACTUAL SCALE (RS1, ACTUAL SCALE (RS1,

RS2, RS3, RS4) OF RS2, RS3, RS4) OF THE WIND THE WIND DAMAGED DAMAGED BUILDING BUILDING

STRUCTURESSTRUCTURES

METHODOLOGYMETHODOLOGY

SATELLITE SATELLITE IMAGESIMAGES

GROUND GROUND TRUTH TRUTH DATADATA

EXTRACTION OF EXTRACTION OF BUILDING BUILDING

STRUCTURESSTRUCTURES

BUILDING INSPECTIONBUILDING INSPECTION

VISUAL VISUAL RECOGNITIONRECOGNITION

FOR FOR TRAININGTRAINING

FOR FOR TESTINGTESTING

DAMAGE RECOGNITIONDAMAGE RECOGNITION

PIXEL RADIANCE PIXEL RADIANCE DATADATA

FEATURE FEATURE EXTRACTIONEXTRACTION

ARTIFICIAL NEURAL ARTIFICIAL NEURAL NETWORK TRAININGNETWORK TRAINING

TRAINED TRAINED ARTIFICIAL NEURAL ARTIFICIAL NEURAL

NETWORKNETWORK

DATA ACQUISITIONDATA ACQUISITION

CLASSIFICATIONCLASSIFICATIONVALIDATION

DATA ACQUISITIONDATA ACQUISITION2004/03/23 PUNTA GORDA 2004/08/14 PUNTA GORDA

Satellite Satellite DataData

GroundGroundTruthTruthDataData

Before HurricaneBefore Hurricane After HurricaneAfter Hurricane

Courtesy :Courtesy :Womble, J.A., 2005. Womble, J.A., 2005.

TTUTTU

Source: DigitalGlobe Co., Ltd, Source: DigitalGlobe Co., Ltd, Hurricane CharleyHurricane Charley

SYSTEM RECOGNITIONSYSTEM RECOGNITION1. 1. Extraction of Building StructuresExtraction of Building Structures

---- 40 samples (houses)40 samples (houses)2. Visual Recognition of Samples Extracted2. Visual Recognition of Samples Extracted

---- Categorized into 4 Four Damage Scales Categorized into 4 Four Damage Scales (RS1, RS2, RS3, RS4)(RS1, RS2, RS3, RS4)

---- 10 Samples each 10 Samples each ---- With the available Ground Truth Information With the available Ground Truth Information ---- 6 Samples each for Training and 4 6 Samples each for Training and 4

Samples each for ValidationSamples each for Validation

SYSTEM RECOGNITIONSYSTEM RECOGNITION1. Pixel Radiance Data1. Pixel Radiance Data

---- RGB channelsRGB channels---- HSV layersHSV layers

HHSVSV : : LayersLayersVVVV

SSSS

HHHH

SYSTEM RECOGNITIONSYSTEM RECOGNITION2. Feature Extraction2. Feature Extraction

----(1) (1) Deletion of common areaDeletion of common area----(2) Conventional Feature Extraction Method(2) Conventional Feature Extraction Method----(3) Wavelet Extraction Method(3) Wavelet Extraction Method

Hse 1 with Hse 1 with Common Area DeletedCommon Area Deleted

Before and After Before and After DisasterDisaster

FEATURES EXTRACTED FOR BOTH FEATURES EXTRACTED FOR BOTH METHODSMETHODS

---- Statistical FeaturesStatistical Features

---- Image FeaturesImage Features•• Standard DeviationStandard Deviation•• MaximumMaximum

•• EdgeEdge detectiondetection

STATISTICAL FEATURESTATISTICAL FEATURESTANDARD DEVIATIONSTANDARD DEVIATION

Vision LayerBlue ChannelGreen ChannelRed Channel

0

0.2

0.4

0.6

0.8

1

RS1 RS2 RS3 RS4Remote Sensing Scale

Stan

dard

Dev

iatio

n

STATISTICAL FEATURESTATISTICAL FEATUREMAXIMUMMAXIMUM

Vision LayerBlue ChannelGreen ChannelRed Channel

0

0.2

0.4

0.6

0.8

1

RS1 RS2 RS3 RS4Remote Sensing Scale

Max

imum

EDGE DETECTIONEDGE DETECTION•• An edge in an image is An edge in an image is a contour across a contour across

which the which the brightness of the image brightness of the image changes changes suddenly suddenly

WhereWhere ff((ii,,jj)) output image pixel output image pixel hh((ii,,jj)) input image pixelinput image pixelww((mm,,nn)) convolution kernel or a filter convolution kernel or a filter

mask of size (2mask of size (2a+a+1)1) (2(2b+b+1).1).

a

am

b

bnnjmihnmwhwjif ),(),(*),(

EDGE DETECTIONEDGE DETECTION•• Prewitt Operator :Prewitt Operator :Finds edges using the Finds edges using the

Prewitt approximationPrewitt approximation..•• It measures two components. It measures two components.

1. vertical edge component 1. vertical edge component with kernel with kernel wxwx2. horizontal edge component 2. horizontal edge component with kernel with kernel wywy. .

wx wx ==wx wx == wywy ==wywy ==

EgEg:: HseHse 11 RSRS33

y

x

f (x, y)

Pixel of image section under mask

Mask coefficient showing

coordinate arrangements

Image

Image origin Mask

f (x − 1, y − 1)

w (−1, −1) w (−1, 0)

w (0, 0)

w (1, 0)w (1, −1)

w (0, −1)

w (−1, 1)

w (0, 1)

w (1, 1)

f (x − 1, y )

f (x , y − 1) f (x , y ) f (x , y + 1)

f (x + 1, y − 1) f (x + 1, y ) f (x + 1, y + 1)

DISTRIBUTION OF THE DETECTED DISTRIBUTION OF THE DETECTED EDGE PIXEL VALUEEDGE PIXEL VALUE

RS1RS1

RS2RS2RS3RS3

RS4RS4

ANN CLASSIFICATIONANN CLASSIFICATION

ANN CLASSIFICATIONANN CLASSIFICATIONPROCEDUREPROCEDURE

Feed ForwardFeed Forward –– Each input neuron receives Each input neuron receives input signal and broad casts to hidden layer input signal and broad casts to hidden layer and pass it to each output unit.and pass it to each output unit.Back Propagation of errorBack Propagation of error-- Net output is Net output is compared with the target value. Appropriate compared with the target value. Appropriate error is calculated and it is distributed back error is calculated and it is distributed back to the hidden layer to the hidden layer Weights adjusted Weights adjusted -- accordinglyaccordingly

WAVELETSWAVELETSFeature Extracted by Feature Extracted by Wavelet Feature ExtractionWavelet Feature Extraction

2 Dimensional discrete 2 Dimensional discrete Wavelets are usedWavelets are used Family of discrete Wavelets :Family of discrete Wavelets :

Best wavelet Best wavelet ---- the the larger % Margin of larger % Margin of separation separation between the two least different RS between the two least different RS scale (scale (RS1 and RS2RS1 and RS2))

--Daubechies, Biorthogonal, Coiflets, Symlets, Daubechies, Biorthogonal, Coiflets, Symlets, Discrete Meyer Discrete Meyer

WAVELETSWAVELETSBiorthogonalBiorthogonal Wavelets Wavelets ----distribution of distribution of the damaged area i.e. the damaged area i.e. Std DevStd Dev and and damaged edge detectiondamaged edge detectionDaubechiesDaubechies ---- maximum valuemaximum value of the of the damaged area damaged area

where where σσ(RS1) = Average standard deviation (RS1) = Average standard deviation of all the sample images at RS1.of all the sample images at RS1.

100211RS2) & (RS1 separation ofMargin %

RSRS

A 2A 2--D WAVELET ANALYSISD WAVELET ANALYSIS

COMPARISONCOMPARISON–– Statistical FeaturesStatistical FeaturesMAXIMUMMAXIMUM VALUEVALUE –– REDRED BANDBAND

RED Without WaveletWith Wavelet

0

20

40

60

80

RS1&RS2 RS2&RS3 RS3&RS4Remote Sensing Scale

% M

argi

n of

Sep

arat

ion

COMPARISONCOMPARISON–– Statistical FeaturesStatistical FeaturesMAXIMUMMAXIMUM VALUEVALUE –– GREEN BAND

GREENWithout WaveletWith Wavelet

0

10

20

40

60

RS1&RS2 RS2&RS3 RS3&RS4Remote Sensing Scale

% M

argi

n of

Sep

arat

ion

50

30

COMPARISONCOMPARISON–– Statistical FeaturesStatistical FeaturesMAXIMUMMAXIMUM VALUEVALUE –– BLUE BAND

BLUEWithout WaveletWith Wavelet

0

20

40

60

80

RS1&RS2 RS2&RS3 RS3&RS4Remote Sensing Scale

% M

argi

n of

Sep

arat

ion

COMPARISONCOMPARISON–– Statistical FeaturesStatistical FeaturesMAXIMUMMAXIMUM VALUEVALUE –– VISION LAYER

VISION Without WaveletWith Wavelet

0

20

40

60

80

RS1&RS2 RS2&RS3 RS3&RS4Remote Sensing Scale

% M

argi

n of

Sep

arat

ion

Margin of Margin of SeparationSeparation

Without Without WaveletWavelet With WaveletWith Wavelet

Red BandRed BandRS1 and RS2RS1 and RS2 36 %36 % 57 %57 %RS2 and RS3RS2 and RS3 53 %53 % 56 %56 %RS3 and RS4RS3 and RS4 23 %23 % 27 %27 %

Green BandGreen BandRS1 and RS2RS1 and RS2 26 %26 % 34 %34 %RS2 and RS3RS2 and RS3 43 %43 % 53 %53 %RS3 and RS4RS3 and RS4 47 %47 % 56 %56 %

Blue BandBlue BandRS1 and RS2RS1 and RS2 25 % 25 % 32 % 32 % RS2 and RS3RS2 and RS3 39 %39 % 43 %43 %RS3 and RS4RS3 and RS4 54 %54 % 69 %69 %

Statistical Features Statistical Features –– Standard DeviationStandard Deviation

Statistical Features Statistical Features –– Standard DeviationStandard Deviation

VisionVisionRS1 and RS2RS1 and RS2 37 % 37 % 57% 57% RS2 and RS3RS2 and RS3 53 %53 % 56 %56 %RS3 and RS4RS3 and RS4 24 %24 % 28%28%

Higher the Margin of SeparationHigher the Margin of Separation More More AccurateAccurate will be the classificationwill be the classification

IMAGE FEATURES IMAGE FEATURES –– with with and withoutand without waveletwavelet

Eg: Hse 1 Eg: Hse 1 RS3RS3

WITH WITH WAVELETWAVELET

WITHOUT WITHOUT WAVELETWAVELET

When edge detection done with waveletWhen edge detection done with wavelet, , the the detection of nondetection of non--damaged edges as damaged damaged edges as damaged edges are reduced rather than edge detection edges are reduced rather than edge detection using Prewitt operator i.e. error reducedusing Prewitt operator i.e. error reduced

House House NumberNumber

Visual Visual RecognitRecognit

ionion

System RecognitionSystem RecognitionWithout With Without With Wavelet WaveletWavelet Wavelet

Ground Ground Truth Truth DataData

Hse 6Hse 6 RS1RS1 RS1RS1 RS1RS1 NANAHse 8Hse 8 RS1RS1 RS1RS1 RS1RS1 NANAHse 3Hse 3 RS2RS2 RS2RS2 RS2RS2 RS2RS2Hse 4Hse 4 RS2RS2 RS2RS2 RS2RS2 NANAHse 1Hse 1 RS3RS3 RS3RS3 RS3RS3 RS3RS3Hse 2Hse 2 RS3RS3 RS3RS3 RS3RS3 RS3RS3Hse 5 Hse 5 RS4RS4 RS4RS4 RS4RS4 NANAHse 7Hse 7 RS4RS4 RS4RS4 RS4RS4 RS4RS4

TRAINING SAMPLES

House House NumberNumberTESTING TESTING SAMPLESSAMPLES

Visual Visual RecogniRecogni

tiontion

System System RecognitionRecognition

Without With Without With Wavelet WaveletWavelet Wavelet

Ground Ground Truth Truth DataData

Hse 9Hse 9 RS1RS1 RS1RS1 RS1RS1 NANA

Hse 11Hse 11 RS2RS2 RS1RS1 RS2RS2 RS2RS2

Hse 10Hse 10 RS3RS3 RS2RS2 RS4RS4 NANA

Hse 12Hse 12 RS4RS4 RS4RS4 RS4RS4 NANA

CLASSIFICATION RESULTCLASSIFICATION RESULT

CLASSIFICATION RESULTCLASSIFICATION RESULT

RS SCALESRS SCALESWITHOUT WITHOUT WAVELET WAVELET

%%

WITH WITH WAVELWAVELET %ET %

BUILDING BUILDING CONDITIONCONDITION

RS1RS1 100100 100100 No obvious damageNo obvious damage

RS2RS2 6060 9090 Roof shingles Roof shingles removed and Deck removed and Deck

exposedexposed

RS3RS3 5050 5050 Deck removed and Deck removed and RoofRoof structure structure

exposedexposed

RS4RS4 9090 100100 Completely Completely collapsedcollapsed

%Accuracy of Identification of Samples %Accuracy of Identification of Samples

CLASSIFICATION RESULTCLASSIFICATION RESULTActual Actual

DamageDamageResults from Results from

Wavelet Wavelet ExtractionExtraction

Actual RS3Actual RS3Error RS4Error RS4

Classification Result with Wavelet Extracted FeaturesClassification Result with Wavelet Extracted Features

RS1

RS1

RS1

RS1RS4

RS4

RS4RS4

RS3

RS3

RS3

RS3

RS2

RS2

RS2

RS2

CONCLUSIONCONCLUSION1.1. Wind Damage Wind Damage Building StructuresBuilding Structures can be can be

successfully successfully identifiedidentified from the statistical from the statistical and image features extracted from the Preand image features extracted from the Pre--and the Postand the Post-- Satellite Images.Satellite Images.

2.2. Classification of the identified Building Classification of the identified Building Structures into different Structures into different Scales in the Scales in the Remote Sensing PerspectiveRemote Sensing Perspective (RS Scale(RS Scale--RS1,RS2, RS3 and RS4) is successfully RS1,RS2, RS3 and RS4) is successfully obtained.obtained.

3.3. The % Margin of separation between The % Margin of separation between different RS Scale is obtained and it is different RS Scale is obtained and it is observed that features extracted byobserved that features extracted bywavelets wavelets have got a have got a larger Margin of larger Margin of separationseparation..

CONCLUSION Cont………CONCLUSION Cont………4.4. LargerLarger thethe MarginMargin ofof SeparationSeparation MoreMore

AccurateAccurate willwill bebe thethe classificationclassification..5.5. ThusThus anan AccurateAccurate IdentificationIdentification isis donedone byby

usingusing WaveletWavelet FeatureFeature ExtractionExtraction..6.6. PatternPattern forfor thethe followingfollowing DamagedDamaged

PortionsPortions areare recognizedrecognized successfullysuccessfully asas ::(a)(a) TheThe distributiondistribution ofof thethe disasterdisaster areaarea BiorthogonalBiorthogonal WaveletWavelet patternpattern

(b)(b) TheThe peakpeak valuevalue ofof thethe disasterdisaster areaarea DaubechiesDaubechies WaveletWavelet patternpattern

(c)(c) FaultyFaulty oror BrokenBroken edgesedges BiorthogonalBiorthogonal WaveletWavelet patternpattern

ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS This study was funded by This study was funded by MEXTMEXT, Japan, , Japan,

through the Global Center of Excellence through the Global Center of Excellence Program, 2008Program, 2008--2012, which is gratefully 2012, which is gratefully acknowledged.acknowledged.

The authors would also like to thank The authors would also like to thank Dr. Arn Dr. Arn J WombleJ Womble, of Texas Tech., , of Texas Tech., Prof. Ahasan Prof. Ahasan KareemKareem of University of Notre Dame & of University of Notre Dame & Dr. Dr. Masashi MatsuokaMasashi Matsuoka of Earthquake Disaster of Earthquake Disaster Mitigation Research Center, RIKEN, Japan, Mitigation Research Center, RIKEN, Japan, for their valuable suggestions and advice.for their valuable suggestions and advice.