Automated Waterline Extraction for Optimal Land Use

A Case Study in Crete

Lemonia Ragia

Vassilis Paravolidakis

Konstantia Moirogiorgou

and

Michalis Zervakis

School of Environmental Engineering, Technical University of Crete, Chania, Greece

School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece

Keywords: Image Processing, Waterline Extraction, Web Application, Land Use.

Abstract: Coastal areas in Greece play an important role to the economic growth of the country. Especially in the islands

as a major tourist destination, new tourist infrastructure is continuously being built. To optimize land use and

protect current infrastructure, we need to properly define the waterline. In addition, the waterline is changing

dynamically due to climate change and weather conditions, so it has to be monitored. The proposed framework

is focused on the automatic extraction of waterline from aerial images using advanced image processing

techniques. We plan to integrate the Greek cadastral data in the framework and along with spatial data analysis

to be able to proceed with both coastal and cadastral data. Additional functionalities are provided to the

planners, through metrics for distance and surface area calculations, in order to extract useful information for

sustainable region planning of the coastal area. The results can be visualized in the images with the metrics

of interest.

1 INTRODUCTION

Coastal zones are among the most productive areas in

the world, offering a wide variety of valuable habitats

and ecosystems that have always attracted humans

and human activities. Currently, more than 200

million European citizens live near coastlines,

stretching from the North-East Atlantic and the Baltic

to the Mediterranean and Black Sea.

As an example, the island of Crete produces about

one fifth of the GDP of Greece only in the summer

(Synolakis et al., 2008). The economy of the Chania

region in North Western Crete is predominately based

on tourist and agriculture and it is a very famous

tourist destination. This intensive concentration of

population and excessive exploitation of natural

resources puts enormous pressure on our coastal

ecosystems leading to biodiversity loss, habitat

destruction, pollution, as well as conflicts between

potential uses, and space congestion problems. The

rapid economic growth of the coastal areas has driven

the public and private sector to make investments in

infrastructure. The urbanization and development of

these areas has led to the construction of new roads,

small villages, entertainment facilities and

infrastructure for sport activities. The intense touristic

development has led to the environmental

downgrading of the areas near the waterline. The

coastal areas are quite fragile landscapes as they are

also among the most vulnerable to climate change and

natural hazards. Risks include flooding, erosion, sea

level rise, as well as extreme weather events. As

negative factors we have human interventions and

natural phenomena due to the climate change. Around

30% of the Greek coastline is affected by erosion

(Valavanidis and Vlachogianni, 2011). In Greece, the

way spatial planning confronts these phenomena is

inadequate.

In our approach the main value of the research is

to extract automatically the waterline and metric

measurements helping the development planners to

consider the risks for the coastal areas and to plan

further actions.

Waterline extraction is mainly obtained through

satellite images (Liu et al., 2013), (Zhao et al., 2008).

The identification of shoreline from historical and

aerial images using topographic data and digital

image-processing techniques has been addressed in

Boak and Turner, 2005. Aerial photogrammetry has

already been used to provide shoreline information

and shoreline dynamics (Dolan et. al., 1978).

In our approach we use aerial images from HMGS

Greece (HMGS, 2015) and Google Earth (Google

Earth, 2014) to extract automatically the waterline.

Ragia, L., Paravolidakis, V., Moirogiorgou, K. and Zervakis, M.

Automated Waterline Extraction for Optimal Land Use - A Case Study in Crete.

In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2016), pages 99-104

ISBN: 978-989-758-188-5

We demonstrate our approach for the town of

Georgioupoli in North Western Crete (Fig. 1). The

proposed framework includes algorithms to extract

the waterline and provides a graphical user interface

to allow the user to extract spatial information about

the coastal area. The user can measure distances and

areas of interest and the results can be visualized

directly on the images.

Figure 1: The chosen area for the demonstration of our

framework.

In addition, we are working on the extension of

the framework to the connection with the Greek

cadaster in order the user to be able to access and

process cadastral data. The overall task is to

effectively manage and associate spatial information

from aerial images and information extracted from

cadaster. This information can be visualized in the

images with the objects of interest together. The goal

of the framework is to: a) Automatically extract the

waterline from aerial images b) to extract metric

information about area, size for objects of interest and

c) to provide spatial information for coastal

management to people for a sustainable development

of the whole region.

2 PROPOSED METHODOLOGY

The waterline estimation method is based on

advanced image processing techniques and consists

of two main steps. The first step provides an

estimation of the coastline based on region

segmentation using a local thresholding method,

while the second step optimizes the results of the first

step based on edge detection using active contours.

Figure 2 presents the block diagram of the proposed

methodology. Blocks inside the dashed lines refer to

methods of waterline extraction, while blue and gray

boxes refer to the functionalities of the framework.

Initially, a preprocessing step is performed on the

input images to remove noise and enhance strong

edges. Aerial photos usually provide a good spatial

coverage of the coast, but they also are inevitably

distorted by noise. A Gaussian filter is applied to

reduce noise without blurring the edges. Another

issue that usually arises is the presence of clouds and

shadows that can deform the data and make it hard to

process the image correctly. We manage this problem

by using an anisotropic diffusion technique (Perona

and Malik, 1987) that emphasizes strong edges, like

coast area buildings, and enhances them or suppresses

weak edges.

Figure 2: Proposed methodology for waterline extraction

with the extension to connect to cadastral data.

The first step aims to segment the image into two

regions; land and sea. The pixels which are located on

the borders of these two regions will constitute the

waterline. To accomplish this task, we developed a

region segmentation algorithm using local

thresholding. It is preferable to use a local threshold

because a global threshold gives false estimations as

a result of intensity heterogeneity of the image.

More specifically, we divide the image into square

overlapping blocks with B width and check the

bimodality of intensity in each of them (Liu and

Jezek, 2004). For every block, we assume that there

is a mixture of two Gaussian distributions (land and

sea), and we test against the null hypothesis of having

just one overall class. We formulate the likelihood

test based on the corresponding probability destiny

functions.

In practice, if the block passes the test, we expect

that there are two clearly separated areas, i.e. land and

sea, so that we can compute the threshold using

Otsu’s method (Otsu, 1979). This method

automatically performs clustering-based image

thresholding of a grayscale image by maximizing the

weighted between-class variance. In this way,

subareas of land are assigned the value 1, while pixels

within sea areas are assigned the value 0. As a post-

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processing step of this method, a concatenation of

overlapping blocks is performed in order to recreate

the whole image in a binary form and morphological

operations are applied in order to remove small

objects and fill the black holes. Subsequently, we use

the canny edge detector, an edge detection operator,

for the detection of edges in the images. The final

binary image is in the form of a white area that

belongs to land and a black area that belongs to sea.

The result of the canny edge detector refers to

coastline.

Most of the times, the extracted coastline needs

improvement, because of unexpected effects in the

image, such as waves, people and intensity distortion

across the coastline. To be able to handle these issues

we used an open active contour method based on the

classical active contour model (snakes) (Kass et al.,

1998) with free boundary conditions. A snake is an

energy-minimizing spline guided by image forces and

external constrain forces. It consists of an initial

contour C

near to a contour in the image and searches

for deformations of C

which let it move towards the

actual image contour. We implemented an automatic

process using active contours with free boundary

conditions (Shemesh and Ben-Shahar, 2011). We

initialize the curve using the extracted coastline from

the first step and we compute iteratively the next

possible position of the curve following the gradient

of the image defining its edges. After a certain

number of iterations defined by user, the procedure

stops and outputs the estimated waterline.

As far for the framework we developed, its

implementation layout and functionality follow.

Figure 3 presents the screen of the application where

two central windows are devoted to the visualization

of the original (left) and processed (right) image,

respectively. The left section of the application

depicts the parameters of the waterline extraction

algorithm and provides the space for adjusting the

algorithmic process. The right section presents the

functionality of the software in association with the

coastline analysis.

The top left box of the framework refers to the

first method’s parameters. Sigma and filter’s size

affect the Gaussian filter.

Sigma refers to standard deviation of Gaussian

distribution. Increasing the standard deviation the

intensity of the noise is reduced, but also appears high

frequency detail attenuation. We have set the number

of 2 as a default value. A larger size filter,

corresponds to a larger convolution mask, but also

affects the details quality of the image. We have set 7

as a default value. It is optimized for the filter size to

be about 3*sigma+1, because, in this way, almost the

whole Gaussian bell is taken into account. Then, the

user chooses the number of iterations and kappa

value, which refers to anisotropic diffusion. Kappa

controls the sensitivity to edges and it is usually

chosen experimentally (the default value is 8), while

the number of iterations must be 5-15, since a higher

number may result in blurring the true edges (the

default value is 5). Next the block size B is defined.

This size depends on the initial image size, because

every block needs to contain necessary information

for our method. We choose 5-8% of total image size,

with default value 300. Next to the setting of the

parameters, the user can proceed with the first

waterline estimation using region segmentation.

Figure 3: On the left is the original image. On the right, the

estimated waterline can be extracted by finding the borders

between the water (black) and land (white).

When the first step is completed, the user chooses

the parameters for the second step. Alpha parameter

controls the internal energy function’s sensitivity to

the amount of stretch in the snake (elasticity). A large

value for alpha decreases the possibility the snake to

change and so the method’s efficiency, so normally

alpha value should be less than 1. In our case, we have

set it to 0.7. Iterations must be defined properly,

because there will be a point that no significant

energy differences are detected. A proper number of

iteration is 100-300. We also provide an optional

threshold to control the energy differences. If the

deference between two consecutive energy values is

below the threshold, the process stops and the final

result is shown at the images section.

As far for the cadastral data association with the

extracted waterline results, in our application there is

a link, called ‘ktimatologio.gr’ (which means

cadaster), that opens the map of Greece in the internet

browser and the user can measure real world

distances, such as buildings. Then by pressing the

‘calibration’ button the user can choose two points

from the initial image which contain the same

building or any other chosen area. A textbox then

opens to enter the known distance and the

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101

measurement unit (Fig. 4). Now, we are able to

compute the distance per pixel (pixel factor) by

dividing the inserted distance by total number of

pixels contained between two chosen points.

Figure 4: (ktimatologio.gr-calibration). The green box

shows the selected area (tennis court) from ktimatologio.gr,

the Blue box shows the same area from aerial image. We

can also see the text box where we import the real units and

the distance.

Figure 5: The computed length of the estimated waterline.

Moreover, the user is able to perform waterline

length calculation by multiplying the total number of

pixels that belong to the waterline by the pixel factor

computed above. The measurement units are the same

as the user has inserted at the previous step (Fig. 5).

In addition, the option to compute the area and

perimeter of a surface of interest, like area of

buildings, distance from waterline or beach surface

area, is provided by pressing the coast surface button.

A new window opens where the user can select a

polygonal area at the image. The task performed is the

computation of the total number of the pixels which

belong to the perimeter, then the multiplication by the

pixel factor in order to show the perimeter in real

world units.

Finally, the computation of the total number of the

pixels which belong inside this polygonal area is

multiplied by the square of pixel factor in order to

show the surface. Figure 6 displays an example of this

operation.

Figure 6: Computed perimeter and surface of an area

selected by the user.

Another similar function provided is the

calculation of the distance between two points. By

pressing calculate distance, a new window opens

where the user can choose two points of the image.

Αs in the previous case, we compute the total number

of the pixels that belong to the chosen line, multiply

by pixel factor and show the results (Fig. 7). This can

be useful, if we have images of the same area but from

different time periods and want to track the erosion of

the coastline.

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Figure 7: Computed distance of a straight line selected by

the user.

As a final feature of the application, the user can

load two images from different time periods of the

same area by pressing the ‘Waterline changes’ button.

When the images load finishes, the two estimated

waterlines are shown in the same figure, thus the user

is able to identify any possible changes (Fig. 8).

Without georeferenced images, it is difficult to work

with aerial images. This feature works better with

google earth images.

Figure 8: The waterline differences between 2003 and

2010. The dashed line refers to 2003 image, while the solid

line refers to 2010 image from Google Earth.

3 CONCLUSIONS

Our work focuses on the waterline extraction and the

connection of the results with cadastral data. The

waterline extraction is performed through advanced

image processing techniques and the results are

compared to the available cadastral data. A user can

load the images of interest, perform coastline

extraction, view the results and, finally, calculate

useful parameters with distance or area metrics. Since

the framework back-end structure is ready, as a next

step we need to focus on the matching of the aerial

images with the cadastral data in order to finalize the

accuracy estimation of our methodology

As future work, our framework needs to be tested

in more than one administrative region in order to

explore the sensitivity and the performance of the

proposed methods and how these two factors are

affected by image parameters, like image resolution

or image data complexity. We should also further

explore the application’s functionality regarding the

cadastral data.

ACKNOWLEDGEMENTS

This work is partially supported by the project

ASTARTE (Assessment, STrategy And Risk

Reduction for Tsunamis in Europe) FP7-ENV2013

6.4-3, Grant 603839 to the Technical University of

Crete, Greece.

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