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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: radhikasabareesh@arch.tContact: radhikasabareesh@arch.t--kougei.ac.jpkougei.ac.jp

    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 damageNo 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 exposedNonlinear, 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 (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 separationsepar...