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A new morphology algorithm for shoreline extraction from

DEM data

Amr Hussein Yousef, Khan Iftekharuddin and Mohammad Karim

Department of Electrical and Computer Engineering

Old Dominion University, Norfolk, VA 23529

ABSTRACT

Digital elevation models (DEMs) are a digital representation of elevations at regularly spaced points. Theyprovide an accurate tool to extract the shoreline profiles. One of the emerging sources of creating them is lightdetection and ranging (LiDAR) that can capture a highly dense cloud points with high resolution that canreach 15 cm and 100 cm in the vertical and horizontal directions respectively in short periods of time. In thispaper we present a multi-step morphological algorithm to extract shorelines locations from the DEM data anda predefined tidal datum. Unlike similar approaches, it utilizes Lowess nonparametric regression to estimatethe missing values within the DEM file. Also, it will detect and eliminate the outliers and errors that resultfrom waves, ships, etc by means of anomality test with neighborhood constrains. Because, there might be somesignificant broken regions such as branches and islands, it utilizes a constrained morphological open and closeto reduce these artifacts that can affect the extracted shorelines. In addition, it eliminates docks, bridges andfishing piers along the extracted shorelines by means of Hough transform. Based on a specific tidal datum, thealgorithm will segment the DEM data into water and land objects. Without sacrificing the accuracy and thespatial details of the extracted boundaries, the algorithm should smooth and extract the shoreline profiles bytracing the boundary pixels between the land and the water segments. For given tidal values, we qualitativelyassess the visual quality of the extracted shorelines by superimposing them on the available aerial photographs.

1. INTRODUCTION

Shoreline is a spatial varying separation between water and land.1,2 Its erosion and accretion play an essentialfactor for coastal resources planning. Shoreline analysis has many applications such as: coastal protectiondesign, sea level rise monitoring, historical rate of change quantification and coastal zones developments policiesformulation.3,4 The mapping of a shoreline is based on a selection of good feature that can robustly handle thetemporal and spatial variations of its positions within the available data sources. Shoreline indicators are aspectsor attributes that can be used to extract the shoreline positions from the available data sources. They can bedivided into two categories:1 (1) visual coastal features indicators and (2) numeric tidal datum indicators. Thevisual indicators are features that can be seen such as wet/dry line while the tidal datum indicators are producedby intersecting the coastal profile elevations with a datum measured from gage stations within the study areasuch as mean high water (MHW). The utilization of these indicators varies with the available data sources alongwith the used approach. In the literature,1 there are around 45 indicators and six of them utilizes digital imageprocessing techniques in extracting the shoreline positions. These digital image processing based indicators are:high-water line, wet-dry line, Shore Line Intensity Maximum (SLIM), Pixel Intensity Clustering(PIC) shore line,Colour channel divergence (CCD), Artificial Neural Network (ANN) shoreline. Table 1 has a brief summary ofthese well known indicators.

Several data sources can be used to extract the shoreline locations such as: historical land-based photographs,coastal maps and charts, aerial images, beach surveys, multispectral/hyperspectral images, LiDAR DEM dataand microwave sensors. Aerial images provide a good data source and they have the advantage of broad spatialcoverage but its temporal coverage is limited to the time of acquisition. In addition, it might be distorted withradial, affine of projective distortions caused by the change in the pitch, yaw or roll of the acquisition sensor field

Contact: Amr Yousef ([email protected]) is a post doctorate research assistant at the Computational Intelligenceand Vision Lab, at Old Dominion University (ODU). Khan Iftekharuddin ([email protected] ) is professor at ODU.Mohammad Karim ([email protected]) is the vice President for Research of ODU.

Optical Pattern Recognition XXIV, edited by David Casasent, Tien-Hsin Chao, Proc. of SPIE Vol. 8748, 87480D · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2015801

Proc. of SPIE Vol. 8748 87480D-1

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Table 1: Shoreline indicators based on digital image processing techniques

Shoreline Indicator Brief Description

High water line5 It is used to extract regions of high brightness gradientPIC shoreline6,7 It differentiates between wet and dry pixels by mapping their

intensities to the HSV color space where the wet and dry pixelsare well clustered.

CCD shoreline6 It separates between the wet and dry pixels based on the diver-gence of the relative intensity in the RGB space

ANN shoreline6 It utilizes an artificial neural network approach to differentiatebetween the wet and dry beach pixels in the RGB space andsegment them into binary representation

Wet-dry line1 It utilized the difference between the edge intensities in the wetand dry regions along the beach

SLIM indicator8 It uses a sequence of images captured during tidal cycle to ex-tract bands of maximum brightness that results from the breakingwaves at the shore to map the contor of the beach surface

of view during the flight time.9 The hyperspectral images provide broad spectral coverage but it’s limited due tosmall pixel resolution. LiDAR data can cover wide spread of regions in short periods of time and they providehigh resolution digital elevation models that are accurate and cost effective. Unlike the aerial photographs thatutilizes the wet-dry boundary as a shoreline proxy affected by tidal effects and wave movements, LiDAR DEMdata can be used to extract true shoreline positions as they are referenced to tidal datum gauge measurements.10

In addition, the LiDAR DEM data can be used to extract shorelines referenced to different datums such asMHW, mean low water (MLW), mean higher high water (MHHW), mean lower low water (MLLW) and meansurface water (MSW) which can’t be determined through aerial images or hyperspectral images.10

The extraction of shoreline from LiDAR DEM data can be obtained in varying ways. Stockdon et al.11

and Morton et al.12 used the cross-shore profile method to locate the shoreline positions. Parker13 utilizeda contouring method to estimate the shoreline positions by segmenting the DEM files into binary image witha reference of tidal datum. Although this method is easy to implement, it produces excessive noisy brokenshorelines especially when the LiDAR DEM files have noise, outliers and errors in the measurements due to highintensity reflections or glints from the water. Liu et al.14 developed a routine that extracts the shoreline fromLiDAR data using a multi-stage morphological operations. After segmenting the DEM into binary images, theydo a set of morphological openings to eliminate pixels that are smaller than the structural elements and closingsto close holes and small areas respectively.15–17 Then, using small thresholds they take two scans to removesmall areas of water and land. Their approach is unable to extract shorelines that are complex as in Figure1 (a)and (b) or the DEM data include measurements error as in Figure 1(c) where a portion of the DEM values nearthe beach are biased due to reflections from water and wave effects and can be misclassified as land objects. Inthis paper we developed an multi-step approach based on morphological operators that can deal with complexshoreline profiles. First, it estimates the missing elevations within DEM data using a nonparametric regressionmethod. Then it reduces outliers and noises by means of anomality detection. A constrained morphologicalopen and close operations are utilized to handle the broken branches and land regions. In addition it eliminatesman-made structures from the extracted shoreline by means of Hough transform.

The rest of this paper is organized as follows: In Section 2 and its subsections, we describe the proposedapproach and introduce its significance. Section 3 discusses the results introduced in this work while section 4conclude the study.

2. SHORELINE DETECTION

Figure 2 depicts the pipeline of the proposed approach. It starts with Data preparation followed by segmentationthat converts the DEM file into binary image. Then, a scan for data outliers and noises is performed. Constrained



-

t4%

(a) Example 1 (b) Example 2

(c) Example 3

Figure 1: Different complex shoreline profiles in DEM data.

morphological open and closing is carried out to fix the broken parts of land or water branches. The unwantedsmall objects are removed followed by a step for cleaning docks, fishing piers and loading docks using Houghtransform. Finally, the shoreline is smoothed to reduce the jagged edges and extracted and superimposed on acorresponding aerial image for visual quality assessment.

2.1 DEM and tidal datum preparations

The LIDAR data is a cloud of irregularly distributed points with X, Y, Z coordinates. Generally, to cre-ate digital elevation models, the LIDAR point cloud is processed in several steps. The cloud data is fil-tered and interpolated into a grid with the required spatial resolution. If one or more LIDAR points arefound in a grid cell, the median Z for those points is taken as the value for the grid cell. For any grid cellwhere no LIDAR points are found, a Z value is determined using an inverse distance weighted interpolationwith the surrounding neighboring points. The horizontal co-ordinates of the LIDAR points are referenced tothe NAD83 datum. In our study, we downloaded the DEM data from the NOAA Coastal Services Center(http://http://csc.noaa.gov/digitalcoast/data/coastallidar/index.html) with point density of 0.1 to 8 pts/meter2

and elevations accuracy of 30 centimeters at 95% confidence interval.

In the processed DEM files, we found segments with not a number (NAN) values missing from LiDAR dataacquisition and these NANs will affect the subsequent steps of extracting the shorelines. To fix this we usedlocally weighted scatterplot smoothing (Lowess18) nonparametric regression method to estimate the LiDAR dataof the NAN elevations regions based on their neighborhoods. We limit Lowess to work on a window that is 4



LiDAR data and

datum preparation *

Extracted shoreline and

superimposing on aerial photo

Segmentation

0

*

OBoundary

smoothing

Anomality

detection

Small area

removal

Constrained

open and close

Man -made

structure removal

times as the NAN region and centered around it. The advantages of using Lowess method are that it doesn’trequire a specific model to fit all the data and it is flexible to handle complex models within data sample.

Tidal datums are elevations that are collected by averaging a sequence of time observations. Legal shorelineis defined by MHW which is the average of all high water heights of each tidal day. The best time to acquireLiDAR data for the area under study is when the water is at its low tide (at spring tides). This enables toderive more than one shoreline such as MHW, MHHW, MSLL, MLW and MLLW which is an advantage overthe aerial photographs. Tidal datum values of the study areas are available from NOAA tides and currentsdatabase (http://tidesandcurrents.noaa.gov/). The tidal datum values are given relative to MLLW and shouldbe converted to North American Vertical Datum of 1988 (NAVD88) by using, a software tool, VDatum, developedby National Geodetic Survey (NGS).2 If the study area is large and has more than one tidal datum, then a twodimensional tidal datum surface should be estimated using the available datums from the gauage stations asusing one tidal datum for large study areas may lead to a significant error in the extracted shorelines.

2.2 DEM segmentation

After both the DEM data and the available datum are prepared and geo-referenced to NAVD88, they’are fedinto a binary thresholding step where the tidal datum should intersect the DEM data. Whenever the DEMelevations are larger than the tidal values they are set to 1 otherwise they are set to zero.

Figure 2: Pipeline of the proposed shoreline detection process.

2.3 Outliers and noise removal

In this step we remove the land (water) outliers pixels from a region that is mostly dominated by water (land)pixels based on their actual elevation values derived from the original DEM data. The process is illustrated inFigure 3.

2.3.1 Land outliers removal

We assume that the water elevations follow a Gaussian distribution. We train the distribution with the detectedwater pixels and test the anomality of the other pixels against it. A window of dimensions m× n is moved overboth the segmented DEM data and the original one and is selected for anomality detection test if the majorityof its elements are water. If the Original data window is x(i, j) and let xw(s, t) ⊂ x(i, j) is a subset of x(i, j) thatcontains the water pixels where their corresponding indices are obtained from the segmented DEM data whiletheir elevations are obtained from the original DEM window, then the mean and the standard deviation of thewater pixels are given by

µw = µ(xw(s, t)) and σw = σ(xw(s, t)) (1)

where µw and σw are the mean and the standard deviation of the water pixels inside the segmented window.From the mean and standard deviation we calculate a threshold of µw +k σw. Every land pixel should be testedfor anomality, i.e., it belongs to the water distribution or not. If the land elevation is less than the thresholdthen it should be inverted to be a water pixel. If it does not belong to the distribution, i.e. its value is greater



than the threshold, then there is a chance to be outliers or a true land pixels that belong to a neighborhoodwindow. A neighborhood test is carried out for the pixel under test to see if it belongs to adjacent land regions.We construct a 2×2 window around the pixel and every window is of dimension k× l. We test the distribution ofwater and land within the 4 adjacent windows. If two or three adjacent windows have a majority of 90% of landpixels, then the tested pixel has a majority neighborhood voting and is decalred as land. If the neighborhoodvotings are 1 or 4, then there a good possibility that the tested pixel is an outlier and should be inverted toa water pixel. In addition, we test the distance between the pixel under test and the nearest pixels inside theneighborhood windows. We count the neighborhood connectivity if the measured distance is sufficiently small(less than three pixels). The basic assumption behind this step is that we assume that the elevations valueswithin a window under test should change smoothly and if there is an abrupt change, it can be related to noisesor outliers. This step is illustrated in in Figure 4(a).

Figure 3: Outliers removal description. A is the original DEM array, AS is the segmented DEM array, w is thewindow under test, x is the pixel under test, n1-n4 are the neighborhood windows, The arrows show the pairwisecorrespondence between the original and the segmented DEM in calculations of the mean and the standarddeviation.

2.3.2 Water outliers removal

Similarly, for a window that is dominated mostly with land pixels, we use the land pixels elevations to derivethe mean and the standard deviation for the Gaussian distribution. We compare the water pixel elevationsagainst a threshold of µl +k σl, µw and σw are the mean and the standard deviation of the identified landpixels inside the window under test. If the water pixel elevation is greater than the threshold then it should beinverted to water pixel. If it is less than the threshold, then there is a chance to be true water pixel. Similarto the neighborhood test for land outliers, we keep the pixel as water if its neighborhood connections are 2 or 3otherwise we inverted into land pixel. Figure 4(b) illustrates this step. Figure 5 show the effects of applying thisstep on one of the DEM files that contains a significant part of measurement errors (check the top part of theoriginal DEM sub-figure). After applying the anomlity detection step, we reduce these noises and outliers whilekeeping the profile of the segmented profile with very minor erosions and dilations. The significance of this stepis the reduction of the pixels that are close to the actual beach and can be misclassified as land pixels. Thesefalse pixels can be grouped together with further morphological operators such as closings and opening and beconnected to the actual beach thus resulting in false shoreline segments.

2.4 Constrained morphological open and close operations

In this step, most of the outliers have been eliminated but in some cases there would be separations or gapsbetween two land regions or we might have some broken branches so by applying small area removals theywould be eliminated and affect the extracted shoreline. Figure 6 (a) shows an example of a DEM file that hasbroken branches and some isolated land regions. We constrained the morphological open and close operationsto work on a window of the segmented DEM file instead of the whole file. By selecting an appropriate windowsize that would be close to broken areas, we calculate the distribution of land and water pixels and apply thecorresponding morphological opening and closing. We used the open operations with a disk structural elementto clear and connect the broken branches and the close operation with structural element of line will be used



Water pixel Elevation

Less than threshold

4 Conn neighborhoods test

Greater than threshold

Inverted to Land

Land pixel Elevation

Greater than threshold

4 Conn. neighborhoods test

Less than threshold

Invested to water

(a) Land removal criterion (b) Water removal criterion

Figure 4: Decision criteria for land and water outliers.

(a) Original DEM (b) Segmented DEM (c) Outliers and noise removal results

Figure 5: Outliers and noises removal results.

to connect separated land regions. We set the radius of the structural disk element to be proportional to thewidth of branches and the length and the angle of the structural line proportional to separation between landregions and their inclined angles. Figure 6 (b) and (c) show the results of applying the constrained open andclose operations and direct unconstrained open and close respectively. We used the same structural elements inthe comparison and it can be seen that our approach succeeded in clearing most of the clogged branches andeffectively connect some of the isolated neighborhood land objects while the direct applying of open and closefailing in these two situations.

2.5 Man-made structures removal

We can eliminate man-made structures such as bridge, fishing piers, docks, etc from the extracted shoreline bymeans of Hough transform.19 We apply the transform to the DEM files and we extract a set of lines that workas structure candidates because Hough transform will not only detect the structures but can also detect someflat regions of the actual shoreline. We decrease the threshold on the number of peaks to be detected on theHough space as well as the detected line length as the structure might not appear as an ideal line and it mightbe broken. By carrying out this step, we generate large set of line candidates that should be filtered out toextract the true structures. With a sliding neighborhood difference scan we can identify the correct segmentsthat represent the real structures and clear them from the extracted shoreline profiles. The rejection criterionis based on using a moving column or row over the selected candidate with its center sliding over the candidatepixels. In, ideal cases and if the candidate is part of the beach shoreline and its pixels are edge pixels, then thedifference between the pixel values after and before the column (row) center should be zero (see Figure 7(a)).In realistic cases of the extracted lines (see Figure 7(b)) the difference will not be zero and it is not significantunless this line segment belongs to a structure such as bridge or fishing pier. Also, it worth noting that alongeach slide there would be only one edge detected across the whole column (row). If the detected line segment ispart of a structure then the difference would be large as its edges are no longer boundary edges between beachand water and around the detected line and there would be two edges across the column (row) with each slide.This method would be effective as Hough transform may detect only small distorted segment of the structures



Mt

II

iÿ_

ji

I1

No.1

(a) Segmented DEM before processing. (b) Processed DEM with constrained close and open

(c) Processed DEM with regular open and close

Figure 6: Constrained Open and close results.

and can be misclassified as true shorelines. Once, the true structures are identified, they are cleared from theshoreline.

2.6 Shoreline Extraction

The last step is to extract the boundary edges between water and land and superimpose their location on thecorresponding aerial image for visual quality identification. To smooth the extracted boundary we convolve theedge detected boundary with a Gaussian kernel to reduce the jagged edges without reducing the accuracy of theextracted shorelines.

(a) Ideal shoreline boundary. (b) Real shoreline boundary

Figure 7: Detection process for man-made structures from Hough lines candidates.



, .

(a) Extracted shoreline with a pier. (b) Detected line segments (c) Processed DEM with regular openand close

Figure 8: Shoreline without pier.

3. RESULTS AND ANALYSIS

We downloaded both LiDAR DEM and the corresponding aerial images in Figure 9 and Figure 10 from NOAACoastal Services Center (http://http://csc.noaa.gov/digitalcoast/data/coastallidar/index.html) and the corre-sponding MHW tidal datum from NOAA tides and currents database (http://tidesandcurrents.noaa.gov/). Wefound the closest measuring stations for Example 1 is station number 8534770 with MHW of 0.637 m duringthe month of July 2011 while the station number 8659897 for example 2 with MHW of 0.461 during the monthof January 2001. Both the DEM files and the tidal datums are referenced to NAVD88. The LiDAR data werecollected by the NOAA National Geodetic Survey Remote Sensing Division using a Riegl Q680i-D system andthey were in Universal Transverse Mercator (UTM), Zone 18 coordinates. The first examples extends from-74.4963637 W to -74.4519713 E and from 39.3611487 N to 39.3291414 S while the second example extendsfrom -78.5830117 W to -78.519994 E and from 33.8890331 N to 33.8448319 S. The horizontal accuracy for bothexamples is 1 m while the vertical accuracy is 0.3 m for the first example and 0.2 m for the second one. The DEMfor the first example is a raster file of z values with 1925 columns and 1790 rows while the second example is 1503columns and 1278 rows. Both the DEM files are horizontally and vertically referenced with respect to NAD83and NAVD88 respectively. It can be seen from Figure 9 (c) and Figure 10 (c) that the extracted shorelines willfollow the visual indicator of the wet-dry boundary of the beach. In some cases, where the water submerge theislands it would be difficult to assess the extracted shorelines without a ground truth. In future work we aregoing to assess the extracted shorelines by means of either the availability of ground control points that shouldbe available through ground surveys or by using Monte Carol simulations where the DEM data is perturbed bynoise and the extracted shorelines will be compared against a baseline to estimate the mean and the standarddeviation of the error in the extracted shoreline.

4. CONCLUSIONS AND FUTURE WORK

In this paper we presented an efficient approach to extract shorelines from LiDAR DEM data and a user predefinedvalue of MHW which represents the legal definition for shorelines in the US. The presented technique estimatethe missing NAN elevations by means of Lowess nonparametric regression method. Then it intersects the DEMelevations with a predefined MHW and segments the DEM data into binary image. Outliers and error in DEMvales are reduced by means of anomality detection constrained on the neighorhood of the processed pixels.Constrained morphological open and close operations are utilized to reduce some artifacts within the data suchas broken branches and land. Based on Hough transform and the weights of neighborhood, it should eliminateman made structures such as bridges, fishing piers and docks. The visual quality assessment of the extractedshoreline is depicted by superimposing the extracted shorelines on their corresponding aerial images.



(a) DEM data. (b) Corresponding Aerial image (c) Extracted shoreline superimposedon an aerial image

Figure 9: Extracted Shoreline for the coordinates:-74.9833677576 W:-74.3829840454E and 39.4034630732 N:38.9064477891S

(a) DEM data. (b) Corresponding Aerial image (c) Extracted shoreline superimposedon an aerial image

Figure 10: Extracted Shoreline for the coordinates: -98.438055W: -77.095555E and 34.709166 N: 25.145833S.



REFERENCES

1. E. H. Boak and I. L. Turner, “Shoreline definition and detection: A review,” Journal of Coastal Re-search 21(4), pp. 688–703, 2005.

2. B. B. Parker, “The difficulties in measuring a consistently defined shorelinethe problem of vertical referenc-ing,” Journal of Coastal Research 38, pp. 44–56, 2003.

3. D. Bellomo, M. J. Pajak, and J. Sparks, “Coastal flood hazards and the national flood insurance program,”Journal of Coastal Research 28, pp. 21–26, 1999.

4. S. P. Leatherman and F. J. Anders, “Mapping and managing coastal erosion hazards in new york,” Journalof Coastal Research 28, pp. 34–42, 1999.

5. M. Shoshany and A. Degani, “Shoreline detection by digital image processing of aerial photography,” Journalof Coastal Research 8(1), pp. pp. 29–34, 1992.

6. S. Aarninkhof, Nearshore Bathymetry Derived from Video Imagery. PhD thesis, Delft University of Tech-nology, Netherlands, 2003.

7. S. Aarninkhof, M. Caljouw, and M. Stive, “Videobased, quantitative assessment of intertidal beach variabil-ity,” in 27th International Conference on Coastal Engineering (Sydney, Australia), p. 32913304, 2001.

8. N. G. Plant and R. A. Holman, “Intertidal beach profile estimation using video images,”Marine Geology 140,pp. 1 – 24, 1997.

9. L. J. Moore, “Shoreline mapping techniques,” Journal of Coastal Research 16(1), pp. pp. 111–124, 2000.

10. H. Liu, D. Sherman, and S. Gu, “Automated extraction of shorelines from airborne light detection andranging data and accuracy assessment based on monte carlo simulation,” Journal of Coastal Research 23(6),pp. pp. 1359–1369, 2007.

11. H. F. Stockdon, A. H. S. Jr., J. H. List, and R. A. Holman, “Estimation of shoreline position and changeusing airborne topographic lidar data,” Journal of Coastal Research 18(3), pp. pp. 502–513, 2002.

12. R. A. Morton, T. Miller, and L. Moore, “Historical shoreline changes along the us gulf of mexico: A summaryof recent shoreline comparisons and analyses,” Journal of Coastal Research 21(4), pp. pp. 704–709, 2005.

13. B. B. Parker, “The difficulties in measuring a consistently defined shorelinethe problem of vertical referenc-ing,” Journal of Coastal Research , pp. pp. 44–56, 2003.

14. H. Liu, “Algorithmic Foundation and Software Tools for Extracting Shoreline Features from Remote SensingImagery and LiDAR Data,” Journal of Geographic Information System 3, pp. 99–119, 2011.

15. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, Chapman & Hall,2 ed., 1998.

16. J. Serra, Image Analysis and Mathematical Morphology, Academic Press, Inc., Orlando, FL, USA, 1983.

17. P. Soille, Morphological Image Analysis: Principles and Applications, Springer-Verlag New York, Inc., Se-caucus, NJ, USA, 2 ed., 2003.

18. W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots,” Journal of the AmericanStatistical Association 74(368), pp. pp. 829–836, 1979.

19. E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall PTR, Upper SaddleRiver, NJ, USA, 1998.



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