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IGARSS 2011 Annual ConferenceADAPTIVE MORPHOLOGICAL FILTERING FOR DEM GENERATIONKyoHyouk Kim and Jie Shan

Geomatics EngineeringSchool of Civil EngineeringPurdue University July 27th 2011Im going to talk about roof segmentation based1IntroductionPrevious WorksProposed ApproachTestsSummary

2/28OUTLINEOUTLINEIntroductionLiDARFilteringPrevious WorksProposed ApproachTestsSummary3/28INTRODUCTIONLiDAR (Light Detect And Ranging)One of remote sensing technologies providing point clouds with highly accurate collections of (X,Y,Z) at observed points.Vertical accuracy : 6 to 30 cmHorizontal accuracy : 10 to 46 cmUsed as one of primary data sources to applications:DEM (Digital Elevation Model)Forest mapping Building footprints & 3D building reconstruction4/284FilteringSeparation between ground points and nonground points.Ground points : Digital Elevation Model (DEM).Non-ground points : Building models, forest structure, canopy mappingIn nearly all applications, filtering should be done first.Various filtering algorithms have been proposed.Slope-based filtersLinear prediction filtersClustering (or segmentation) filtersMorphological filters

INTRODUCTION5/285INTRODUCTIONExample

6/286OUTLINEIntroductionPrevious WorksGeneral ruleMorphological filtering algorithmKnown issues & weaknessesProposed ApproachTestsSummary7/28PREVIOUS WORKSGeneral ruleWhat is ground surface ?Ground surfaces are relatively smooth & continuousLocally spanned through the lowest parts.EX: discontinuous ground surface and negative blunders.All filtering algorithms try toIdentify points on the continuous and smooth ground surfaceRemove points lying on non-ground objectsMeasures of discontinuityAbrupt slope or elevation difference

8/288PREVIOUS WORKS Morphological filteringCommonly used in the image processing fields to :Remove noiseEnhance imagesExtract featuresPublished papers for LiDAR filteringKilian, Halla, and Englich (1996), Kilian et al. (1996), Zhang et al. (2003), Zaksek and Pfeifer (2006), Zhang and Cui (2007), Chen et al. (2007,2009).

9/289PREVIOUS WORKS PrinciplesMorphological opening operation : Erosion followed by dilationErosion : Dilation :

Objects with different sizeApply different window size If terrain is not flatuse maximum slope

10/28dd010PREVIOUS WORKSKnown issues & weaknessesParameters have a great impact on the filtering resultSlope : allowed minimum elevation differenceWindow size : objects with various sizesSmall window : Can not remove larger buildingsLarge window : Highly possible to flatten ground surfaceNo optimal set of parameters supporting various types of terrainRequire a priori information about the target area.Require trial-and-error process to find the best set of parameters

11/2811OUTLINEIntroductionPrevious WorksProposed ApproachAdaptive morphological filteringTestsSummary12/28PROPOSED APPROACHAdaptive morphological filteringUse of regular grid with grid size g ( average point spacing)Cell with multiple points : point with the lowest elevationEmpty cell : elevation of the nearest pointKeeps the indices of original LiDAR points1D (or 2D) Morphological erosion operation (W = 3).Filtering is applied only to points of discontinuity iteratively.Relevant parameters are adaptively adjusted in each iteration13/2813PROPOSED APPROACHUse of 2D regular grid

14/2814PROPOSED APPROACHDiscontinuity MeasureIdentify points lying on the edge between ground and objects

Use of nominal value for thresholds (No fixed thresholds)Ex: 50, 75 or 90 percentileThreshold is determined before each iterationSlopeHeight differenceResidual15/2815PROPOSED APPROACHExample

One LiDAR profileSlopeResidual

16/2816PROPOSED APPROACHWorkflowDetermine residuals (ri) of all pointsIf ri > N percentile of r (N=50,75,90)If hi - (New hi) > Hminhi = New hiUntil # of filtered points = 0For i=1 to # of rows (or # of columns)End17/2817

PROPOSED APPROACHExample70th row

- 50 percentile - Hmin = 1.0mPurdue campus18/2818PROPOSED APPROACHRefinementType I (omission) error : remove ground points mistakenlyCaused by (1) negative blunders and (2) discontinuous groundType II (commission) error : classify objects to ground pointsCaused by (1) nonground points with smaller elevation than Hmin

19/2819PROPOSED APPROACHExample ( Type I error)

20/2820PROPOSED APPROACHResolve Type I errorIdentify continuous segments of ground pointsDistance of any non-ground points to the line (Si(n) Si+1(1))Repeated until no more ground points are restored

d21/2821OUTLINEIntroductionPrevious WorksProposed ApproachTestsSummary of data setsEvaluationsSummary22/2822INITIAL RESULTS23/26Test data sets

Almost flatBuildings mixed with trees

High relief (mixed slope)Dense vegetation

High relief (mixed slope)Small step-wise featuresDiscontinuous surface

23/2823INITIAL RESULTSFiltered ground surface (1)

Hmin = 1.5mResidual threshold = 50 percentile

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INITIAL RESULTS

Filtered ground surface (2)Hmin = 1.0 mResidual threshold = 50 percentile

25/28INITIAL RESULTSFiltered ground surface (3)

Hmin = 1.5 mResidual threshold = 50 percentile

26/28OUTLINEIntroductionPrevious WorksProposed ApproachTestsSummary27/28SUMMARYAdaptive morphological filteringMinimize the effects of parametersFiltering is applied only to points on the boundary iterativelyResidual is used as the measure id discontinuityResidual threshold is adaptively adjusted with nominal valuePromising results from various terrain conditions, but :Type I error is often significant in case of discontinuous terrainNeed more robust stopping criterion

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