An Automated Approach of Shoreline Detection Applied to Digital Videos using Data Mining

Research Journal of Applied Sciences, Engineering and Technology 14(3): 101-111, 2017
DOI:10.19026/rjaset.14.4152
ISSN: 2040-7459; e-ISSN: 2040-7467
© 2017 Maxwell Scientific Organization Corp.
Submitted: November 15, 2016 Accepted: February 13, 2017 Published: March 15, 2017
Corresponding Author: I. Made Oka Widyantara, Electrical Engineering Department, Faculty of Engineering, Udayana
University, Jimbaran, Bali, Indonesia
This work is licensed under a Creative Commons Attribution 4.0 International License (URL: http://creativecommons.org/licenses/by/4.0/).
101
Research Article
An Automated Approach of Shoreline Detection Applied to Digital Videos using
Data Mining
1
I. Made Oka Widyantara, 1
I. Made Dwi Putra Asana, 1
N.M.A.E.D. Wirastuti and
2
Ida Bagus Putu Adnyana
1
Electrical Engineering Department, Faculty of Engineering,
2
Civil Engineering Department, Udayana University, Jimbaran, Bali, Indonesia
Abstract: This study aims to detect a shoreline location and its changes automatically in the temporal resolution.
This approach is implemented on the coastal video monitoring system applications. The proposed method applied
data mining by using two main systems-a training system using classification and shoreline detection systems with
Self-Organizing Map (SOM) and K-Nearest Neighbor (K-NN) algorithms. The training system performs feature
texture extraction using agray-level co-occurrence matrix and the results are stored to classification process. The
detection system has five processing stages: contrast stretching preprocessing and morphological contrast
enhancement, SOM clustering, morphological operations, feature extraction and K-NN classification and detection
shoreline. Preprocessing was used to improve the video image contrast and reliability. SOM algorithm in
segmenting objects in the onshore video images. Morphological operations were applied to eliminate noise on the
objects that were not needed in the spatial domain. The segmentation results of video frames classified by K-NN.
The aim is to provide the class labels on each region segmentation results, namely, sea label, land label and sky
label. The determination of the shoreline is done by scanning the neighboring pixels from the edge of land class
label after binary image transformation. The shoreline change detection was performed by comparing the position of
existing shoreline and shoreline position in the reference video frame. A Receiver Operating Characteristic (ROC)
curve was used to evaluate the performance of shoreline detection systems. The results showed that the combination
of SOM and K-NN was able to detect shoreline and its changes accurately.
Keywords: Feature extraction, image enhancement, K-NN, shoreline, SOM, video monitoring
INTRODUCTION
Shoreline, the contact zone between ocean and
land, has changed over time due to the cross-shore
movement of sediments along the shore and the
dynamic nature of the water surface. The dynamic
changes of the shoreline may cause erosion of the beach
bodies (abrasion) and the addition of beach bodies
(accretion), which can damage the coastal environment.
Therefore, monitoring the position of the shoreline is a
very significant issue given the socio-economic value
and high population density along shore areas
(Goncalves et al., 2012; Huisman et al., 2011).
Beach morphology changes in different spatial and
temporal scales. Therefore, intensive monitoring
schemes are required (Rigos et al., 2014). Traditionally,
shoreline studies have been based on field
measurements of waves, currents, sediment transport
and morphological changes. This scheme can provide
essential information concerning the shoreline in
temporal resolution but it has limitations in spatial
resolution. Moreover, some logistical difficulties, such
as high cost and difficulty of the survey when the
weather conditions are bad, exist in this scheme (Rijn,
2007). Another method is remote sensing by using
satellites or aircraft fitted with remote-sensing systems
with active and passive sensors to describe the
dynamics of the nearshore area. This method offers a
good spatial-temporal resolution, but these observations
are relatively expensive and the visibility is not
extensive.
Some of the difficulties mentioned above can be
solved using video-based remote-sensing systems
(Almar et al., 2012). Video monitoring system provides
information in data per second in the time scale and
spatial scales from meters to kilometers. The emergence
of devices based network cameras, allowing the spatial
scale can be expanded. This is done by installing

Res. J. Appl. Sci. Eng. Technol., 14(3): 101-111, 2017
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cameras scattered along the coast and is integrated in a
computer network.
Currently, many studies on shoreline estimate use
data from digital video sources. Video-detected
shoreline is generally estimated using several methods
among:
• Shore Line Intensity Maximum (SLIM)
• Pixel Intensity Clustering (PIC)
• Artificial Neural Network (ANN)
• Channel Color Divergence (CCD)
SLIM method defines as cross-shore shoreline
position at which wave breaking is maximized, which
corresponds to maximum pixel intensity in close to the
shore (Plant et al., 2007). CCD method is based on red,
green and blue channels whose values are similar to the
white sand on the shore but have different values in
water (Turner et al., 2000). The shoreline is defined on
each cross-shore transect where divergence between
color channels exceeds a threshold. A technique was
developed by PIC (Aarninkhof et al., 2003) that uses
both color and grayscale intensity information. The
ANN models use neural networks to differentiate wet
pixels from dry pixels by using the RGB channel as an
input (Rigos et al., 2014; Kingston et al., 2000). The
number of proposed methods showed that the robust
shoreline detection procedures have many challenges
due to the variety of intra-annual environmental,
hydrodynamic and morphological conditions in the
coastal zone.
In an effort to develop a system that provides
remote sensing-based digital videos, this study has built
shoreline detection systems using data mining
techniques. Detection of shorelines is done by
segmentation of shore objects in the video frame,
followed by the classification of these objects. The
objects on the shore on a video frame are separated
according to the similarity of pixels and the process of
object recognition would categorize objects into objects
land, sea and sky. When an object is recognized as the
land, then the scanning process is carried out to obtain
the pixels in the object marked as land that has pixels
neighboring the sea objects. The pixels on the land label
that has neighboring pixels with sea label is shoreline.
This study applied SOM algorithms in the process of
clustering and K-NN algorithm in the classification
process.
SOM algorithm has been known as a reliable
clustering method and has been applied in a wide range
of applications (Kohonen, 2013; Ortiz et al., 2013).
SOM-ANN training uses a technique that is based on
winner take all, where only the winner neurons
willrenewed its weight. Although SOM is based on
ANN, itdoes not use the class target value, so that no
classes are defined for each data.
As a classifier algorithm, the K-NN algorithm has
advantages when used for the election of large data
compared with other classification methods (Jain et al.,
2000). K-NN algorithms perform classification based
on the proximity (distance) of the data with other data,
which is the shortest distance from a sample to the test
sample. With this technique, the K-NN algorithm is
able to fully exploit the spatial correlation between
adjacent data (Huang et al., 2016). This technique is
needed for the classification of land, sky and sea objects
on ashore line image.
In this study, an automatic shoreline detection in
the video monitoring system of shoreline change was
explained. The proposed methods comprise five
modules. The first module formed preprocessing
mechanisms to improve the shoreline image quality.
The clustering process which segmenting objects that
based on color feature similarity by getting rid of color
feature of pixels, itwas performed in the second
module. The quality of segmentation improved by
removing noise usingthe morphology algorithm was
done in module three. Feature extraction and object
classification was performed in module four. Moreover,
module five provides the shoreline extraction. For the
classification mechanism, this study uses a
classification training method for a shore area.
MATERIALS AND METHODS
In this study, a video monitoring system to track
shoreline changes using data mining techniques has
been developed. The dataset video that was used was
shore area monitoring dataset of Egmond, Netherlands
that was downloaded from the website Deltares Argus
Archive (Argus, 2015). The video was an image
combination that proceeded sequentially in time with a
certain speed. The images that formed the video were
called frames. Moreover, the image recitation speed
was called frame rate and has a unit of frame per
second (Widyantara, 2015). The video frame of the
Egmondshore area was an input that was processed in
the system so that the position shoreline could be
shown automatically. A flow analysis of the video
monitoring system concerning the shoreline change is
shown in Fig. 1.
Preprocessing: The video frames were obtained from
the process of monitoring the activity of the coastline, is
a color image with sunlight effect compositions that
vary according to the acquisition time. The effect of
sunlight varies in shore areas and this becomes a
problem when separating objects during the
segmentation process (Widyantara et al., 2016).
Therefore, an image enhancement mechanism is
required to improve the graphical display of the video
frames prior to the segmentation process.
Image enhancement is accentuated or sharpening
elements of an image such as edge, boundaries or
contrast levels that it creates a useful graphic display of
the image. Image enhancement does not fix or improve

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Fig. 1: Research method
the quality of information and data that already exists in
the image. Nevertheless, image enhancement increase
the dynamic range of desired elements in the image so
that the elements can be noticed or seen clearly. Image
enhancement includes functions such as contrast
manipulation, noise reduction, edge crispening and
sharpening, interpolation and image magnification. The
selection of techniques to be used must also consider
the characteristics of the video image that needs to be
processed. This study usesa technique based on contrast
manipulation, which is a combination of contrast
stretching and morphological contrast enhancement
techniques, to increase the contrast of video frames.
The goal is to enable the process of segmentation to
cluster objects in the video frames.
Contrast stretching: The contrast improvement of
video frames using contrast stretching can be done on
the original image, which means that this process only
depends on the value of intensity (gray level) of a pixel
and is not dependent on other pixels around it. Contrast
stretching will sharpen the elements in the image by
increasing the dynamics of gray level.
In contrast stretching, each pixel in the image is
transformed by:
. (1)
where, u(i,j) and o(i,j) are the pixels before and
aftertransformed on the coordinate (i, j), c and dare the
maximum and minimum values of pixels on the input
image and L is the maximum value of gray. If the pixel
value is less than zero, then it will be zero. Moreover, if
the value is greater than L−1, then it will be L−1 (Al-
Amri, 2011).
Morphological contrast enhancement: The objective
of morphological contrast enhancement is to clarify the
objects in the video frames obtained from the
monitoring of coastal areas. This technique can
overcome the influence of sunlight (illumination); thus,
the quality of image segmentation can be improved
(Widyantara et al., 2016).
Morphological contrast enhancement is applied
using morphology operation, namely, top-hat and
bottom-hat transformation (Hassanpour et al., 2015).
o(i, j) =
u(i, j)− c
d − c
(L −1)

104
Top-hat transformation is a morphological operation
that extracts the bright areas on the video frame using
structure elements. Conversely, bottom-hat
transformation extracts the dark areas. Both
morphology operationsare given as:
( ) ( )THTop Hat A A A A B− = = − ⋅ (2)
( ) ( )BHBottom Hat A A A B A− = = ⋅ − (3)
Further more, the video frame with new contrast is
obtained by summing the video frame with the results
of a top-hat transformation. To optimize the contrast,
the sum of video frames with the results of the top-hat
transformationthen substracted to the results of the
bottom-hat transformation. This process can reduce
dark areas in the video frame. Mathematically, this
process is given by:
EN TH BHA A A A= + − (4)
SOM: SOM algorithm separates objectsin the video
frames based on similarity features in the color of the
pixel, that is, the color index of the Red (R), Green (G)
and Blue (B). Further more, SOM segments the color
index by estimating the proximity to the center pixel
cluster. The SOM algorithm used for segmenting the
video frame is shown in Algorithm 1 (Kohonen, 2013).
SOM is a form of unsupervised ANN topology,
where the training process does not require supervision
(target output). SOM is used for clustering the data
based on the features of the data. Color image
segmentation results using the SOM algorithm can
produce a human perceptual segmentation approach.
Morphological operation: Morphological operation is
used to optimize the segmentation result by eliminating
noise on the objects that are not needed. Basic
operations performed on the morphology are dilation
and erosion. This operation is the basis for making a
variety of morphological operations that are very useful
for processing video frames.
Dilation: Dilation process is done by comparing each
pixel on the input video frame with a central value of
structuring element. This is done by superimposing
structuring element with the video frame, so that the
center of structuring element is to be right with the
pixel position of the video frame. If an object (image
input) is expressed by A, structuring element are
expressed by B and Bx is stated as translational B such
that B lies in the center of x, then the dilation operation
A to B can be expressed by:
{ }( , ) : xD A B A B x B A φ= ⊕ = ∩ ≠ (5)
Algorithm 1: SOM Algorithm
1. Initialization
a. Weight, wji
b. Initial learning rate, learning function, dan number
of iteration.
c. Number of cluster.
2. As long as the maximum number of iterations has
not been reached, do steps 3-7
3. For each input vector x (feature RGB), do steps 4-
4. Calculate all j with:
5. Dj = ∑ (‫ݓ‬௝௜ − ‫ݓ‬௜)௜
2
;i = 1, 2, ...., N
6. Determine the index j such that the minimum Dj
7. For each unit j around J, modification of weight
with:
8. wji(new) = wji (old) + η (xi – wji (old))
9. Modifications learning rate
Erosion: The erosion process is the opposite of
dilation. If the dilation process produces large object,
then the the reverse, the erosion processes will produce
narrowed objects. The hole in the object will appear
enlarged due to narrowing of the object boundary.
Erosion operation is given by:
( ) { }, : xE A B A B x B X= Θ = ⊂ (6)
Gray-Level Co-occurrence Matrix (GLCM): GLCM
is included in statistical methods where the statistical
calculations use gray degrees distribution (histogram).
GLCM statistics is a popular method in the image
texture feature extraction. Based on co-occurrence
matrix, Haralick et al. (1973) defined fourteen texture
features that were measured from a probability matrix
to extract the characteristics of remote-sensing image
texture. In this study, GLCM is used for texture features
extraction of segmentation results. Texture features
were used to identify areas (classification) with values
of land, sea and sky labels. Texture features used were
entropy, energy, contrast and homogeneity and are
defined as (Zayed and Elnemr, 2015):
( , )ln ( , )d d
i j
Entropy P i j P i j= −∑∑ (7)
2
( , )d
i j
Energy P i j= ∑∑ (8)
2
( ) ( , )d
i j
Contrast i j P i j= −∑∑ (9)
2
1
( , )
1 ( )
d
i j
Homogenity p i j
i j
=
+ −
∑∑ (10)
where,
pd = The normalized symmetric GLCM
pd(i,j) = The (i,j)th element of the nomalized GLCM
K-NN: K-NN is an object classification method based
on proximity (distance) of the data with other data. In

105
the classification, there is a training system to assist in
the classification. Training process on the K-NN
method is done by storing the feature of vector images
with a specified label (Kim et al., 2012). K-NN
classification can perform well on image classification
with texture features. K-NN classification algorithm is
shown in Algorithm 2. In this study, Euclidean distance
is used to measure the distance between test data and
training data objects.
Algorithm 2: K-NN classification algorithm:
1. Z = (x’,y’),s the test data as a function of a vector
x’ and the class label is unknown (y')
2. Calculate the distance d(x’,x), which is the distance
between the test data (Z) to each vector training
data, store it in D.
3. Select D2⊆ D, that is K-NNs of Z
4. Calculate the class:
‫́ݕ‬ = arg max ∑ ‫ݒ(ܫ‬ = ‫ݕ‬௜)(௫೔,௬೔)∈஽೥
Shoreline extraction: In this study, the shoreline
extraction process was performed using land label
scanning on the results obtained after K-NN
classification. Scanning of labels made against
neighboring pixels ofthe image of the label area of land.
If the neighboring pixels were not members of the land
label, then the pixels are marked as pixels of the
shoreline. A collection of pixels that were marked as
pixels of the shoreline will form shoreline areas.
RESULTS AND DISCUSSION
The detection of the shoreline position during
video frame monitoring coastal areas using data mining
techniques has been applied in this study using Matlab
R2014a. Figure 2 shows that the shoreline position was
successfully determined using the video frame
monitoring shoreline system. The system consists of
several sub-processes including training systems,
preprocessing, image segmentation, morphological
operations, classification and determination of shoreline
position.
Training system: Figure 3 shows training processes for
training data using K-NN classification. In the training
system, texture feature extraction is performed using
GLCM. Texture is stored with a label that has been
determined using a. csv file that will be used in the
classification process. Figure 4 shows samples of the
training data using the K-NN classification training
system.
Preprocessing: In this process, video frame
enhancement is done using contrast stretching and
morphological contrast stretching so that the color in
the video frame is sharper and the influence of
illumination is reduced on the frame contrast stretching
operation. The results of preprocessing a video frame
are shown in Fig. 5. The Matlab script for contrast
stretching is indicated by
Function imgContrast = contrast(img)
rimg = histeq(img(:,:,1));
gimg = histeq(img(:,:,2));
bimg = histeq(img(:,:,3));
imgContrast = cat(3, rimg, gimg, bimg);
Fig. 2: Shoreline video monitoring systems

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Fig. 3: Shoreline training system classification
(a) (b)
(c)
Fig. 4: Sample of training data; (a): sea label; (b): land label
and; (c): sky label
(a) (b)
(c)
Fig. 5: Preprocessing results of video frame; (a): Original
frame; (b): Contrast stretching; (c): Morphological
contrast enhancement
The syntax for the morphological contrast
enhancement process is as follows:
se = strel(‘square’,10);
Itop = imtophat(I);
Ibot = imbothat(I,se);
Ienhance = imsubtract(imadd(Itop,I),Ibot);
Ienhance = imcompliment(Ienhance);
SOM segmentation: The results of the image
enhancement process undergo the image segmentation
process, which divides the objects in the video frame
into a separate areas. In this process, the similarity of
every region refers to an obvious criteria. In this study,
the criteria used is based on the features of RGB.
Algorithm 3: Segmentation process using SOM:
Input: Input Vectors
Output: Image Cluster
1. Initialize Total Cluster, MaxIterations, Learning
Rate, Learning Rate Reduction
2. xij<- Input Vectors
3. wij<- Initialize Codebook Vectors (Input Vectors
Width, Total Cluster)
4. For (z = 1 to maxiterations)
5. For (y = 1 to Input VectorHeight)
6. For (j = 1 to Total Cluster)
7. Calculate distance Dj = ∑ (‫ݓ‬௜௝ − ‫ݔ‬௜௝)ଶ
௜
8. End
9. BMUz= minimum(D)
10. ind = index of minimum(D)
11. ClusterIndexi = ind
12. Update wij, wi= wi + (Learning Rate*(xind - wi))

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Table 1: SOM algorithm performances
No Testing data Silhouette values Explanation / Criteria
1 0.3610 Weak Cluster
2 0.5447 Reasonable Cluster
3 0.5290 Reasonable Cluster
13. End
14. Learning Rate =
15. Learning Rate*Learning Rate Reduction
16. End
17. PixelLabel <- Reshape (Cluster Index, Input
VectorHeight, Input VectorWidth)
18. For (s = 1 to Total Cluster)
19. Image Clusters <- Input Vectors = Pixel Label(s)
20. End
In the SOM segmentation, feature values of RGB
should be normalized. The aim is that values of feature
has an uniform range of minimum and maximum
values. After the process of normalization, the
segmentation mechanism using SOM was conducted
with several attribute values, i.e., the number of clusters
is 3, the maximum number of iterations is 100, the
learning rate is 0.5, the learning function is 0.2 and the
initialization of the initial weight is randomly
determined according to the number of clusters.
Determination of the attribute values is based on a
series of experiments that can maximize the
performance of the SOM algorithm. The segmentation
process using SOM is shown in Algorithm 3, while the
segmentation results are shown in Fig. 6.
The performance evaluation of the SOM algorithm
in segmenting the video frame on a shoreline detection
system is determined by searching an index value
validation, using the silhouette coefficient (Rousseeuw,
1987). The measurements are carried out against three
randomized trial data.
Table 1 shows that the quality of the segmentation
generated by the SOM algorithm is good, which is
entered in the category of reasonable cluster. It has been
shown that the application of preprocessing techniques
using contrast stretching and morphological contrast
enhancement can help the SOM-based segmentation
process for segmenting objects in the video frames.
Preliminary research has been done also shows that the
application of techniques based on morphological
contrast enhancement is able to indicate the degree of
ownership of the objects in the cluster approach 1
(reasonable). The values of silhouette has a range of -1
to 1, where the value is close to 1 have a better quality
cluster. This means that the contrast manipulation-
based techniques can support the process of
segmentation (Widyantara et al., 2016).
Morphology operation: Application of morphology
operation is to reduce the set of small pixels on the
results of segmentation that can affect the performance
of the classification of the shore area. As shown in Fig.
7, morphological operations are able to optimize the
segmentation results generated by SOM. The
application of the morphology operation carried out at
several stages of the process, namely, a binary image
transformation, opening, closing and imfill. The stages
of implementing techniques of morphology operation
are shown in Algorithm 4. The final results of the
morphology operation are then used as a reference
matrix to optimize the segmentation results for the
classification process.
Algorithm 4: Morphology operation:
1. Binary image transformation BW1 = im2 bw
(segmentation result)
2. Opening operation BWao = bwareaopen(BW1)
3. Closing operation closeBWao = imclose
(BWao,nhood);
4. Filtering Operation rough Mask = imfill (close
BWao,’ holes’);
K-NN Classification: KNN classification aims to
provide a label class in each region segmentation results
of SOM. This study has used three class labels, namely,
sea, land and sky. In this study, the classification
process is carried out in three main stages, namely, the
extraction of features, trainning of dataset and
classification of K-NN. The implementation details of
each stage is shown in Algorithm 5.
K-NN algorithm testing is done by calculating the
area under the curve (AUC) on the ROC curve (Han
et al., 2011). ROC curves are presented on the basis of
the results of testing of each class label a with
confusion matrix. Each class was tested three times,
with a set of 30 testing data. The classification is
evaluated by analyzing the accuracy, sensitivity,
specificity and false positive rate. The results of the
confusion matrix test in each class are presented on the
ROC curve.
Algorithm 5: Classification process:
Training Data:
Input: Sample Data, Label
Output: Training Data

108
(a) (b) (c)
Fig. 6: Segmentation results of video frame; (a): Segmentation 1; (b): Segmentation 2; (c): Segmentation 3
(a) (b) (c)
(d) (e) (f)
Fig. 7: Morphological process; (a): SOM result segmentation; (b): Binary image transformation; (c): Opening operation; (d):
Closing operation; (e): Imfill operation; (f): Segmentation final
1. c = SampleData;
2. l = classLabel;
3. x = extraxt GLCMFeature (c);
4. Define label to x, t = [c x];
5. Store t into TrainingData vector;
6. Return TrainingData
Classification:
Input: Image Cluster, Training Data
Output: Labelled Image Cluster
1. c = ImageCluster;
2. t = TrainingData;
3. For (i = 1 to c.count)
4. x = Extract GLCMFeature(ci);
5. For (j = 1 to t.count)
6. calculate distance Dij(x,t)
7. Dij = ∑ (‫ݓ‬௜௝ − ‫ݔ‬௜௝)௜
2
;
8. End
9. Order D from lowest to highest;
10. Select the K-nearest instance to x : ‫ܦ‬௫
௄
;
Table 2: K-NN algorithm performances
Experiment Accuracy Precision Sensitivity Specificity False positive
Sea label
1 0.90 0.95 0.90 0.90 0.10
2 0.93 0.87 0.93 0.86 0.14
3 1 0.95 1 0.70 0.30
Land label
1 0.87 0.75 0.90 0.85 0.15
2 0.90 0.90 0.95 0.80 0.20
3 0.87 0.82 0.93 0.80 0.20
Sky label
1 0.83 0.86 0.80 0.86 0.14
2 0.87 0.82 0.93 0.80 0.20
3 0.83 0.94 0.80 0.90 0.10

109
(a) (b)
(c)
Fig. 8: ROC Graph; (a): sea label; (b): land label and; (c): sky label
11. LabbeledImageClusteri = Assign to x the most
frequent label in ‫ܦ‬௫
௄
;
12. End
13. Return LabelledImageCluster;
As shown in Table 2 and Fig. 8, in general, the
classification of sea, land and sky labels can be
performed well by using the K-NN algorithm. This is
evidenced by the AUC value that is at an average value
of sensitivity approaches a value of 1, each of 0.94 to
sea label, 0.82 to land label and 0.84 to sky label. In
data mining, AUC value range is from 0.5 up to 1. In
the AUC value of 0.9 up to 1, said that the quality
classification is unbelievably good and at a value from
0.8 to 0.89 classification quality is good (Gorunescu,
2011).
Shoreline detection: In the process of determining
shorelines, the neighboring pixels were scanned from
the edge of the land areas using binary image
transformation. Scanning was performed from the right
side in Fig. 9. If the neighboring pixels had a value of
approximately zero, then the pixel was given a value of
one as a shoreline sign.
To determine the change of shoreline, shoreline
position comparisons are needed between video frames.
This study uses the shoreline position in a reference
frame as the reference position. Furthermore, this

110
(a) (b) (c) (d)
(e) (f) (g) (h)
Fig. 9: Determining shoreline position process on frame 1 and frame 2: (a) and (e) areas with land label, (b) and (f)
transformation results of binary images, (c) and (g) scanning neighboring pixels and (d) and (h) shoreline on a video
frame
(a) (b)
(c)
Fig. 10: Shoreline changes; (a): Reference frame; (b): Data
frame 1 and; (c): Data frame 2
reference position compared to the position of the
shoreline in the other video frame. The results of the
comparison will be displayed in a video frame
reconstruction (Fig. 10). The shoreline found is
provided in red, the shoreline of the reference frame is
in purple and the area of the shoreline change is in pink.
The information of the distance between video frame
inputs and reference frames denotes the number of
pixels in the range of marked areas.
CONCLUSION
The implementation of data mining techniques for
detecting the shoreline location by using frame-by-
frame video monitoring of shore areas has been
performed. The proposed system comprises a
classification training system for a shore area and has a
shoreline detection system that is based on SOM and K-
NN algorithms. This system can automatically detect
the position of the shoreline in the video frame. It can
also display information concerning shoreline alteration
based on the reference frame. The distance information
concerning the changes in a shoreline is in pixels.
Further research is needed to change the video frame to
the map domain using the rectification technique so that
the distance measurement can be performed using
scaling techniques.
ACKNOWLEDGMENT
This study was funded by competitive research
grants in 2016 from Udayana University under the
contract number 486.25/UN14.2/PNL.01.03.00/2016.
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