AN ALGORITHM FOR COASTLINE DETECTION USING SAR IMAGES
U. Acar* , B. Bayram*, F. Balik Sanli*, S.Abdikan* F. Sunar?, H. I. Cetin?,
* YTU, Civil Engineering Faculty, 34220 Esenler Istanbul, Turkey - (uacar, bayram, fbalik, sabdikan,
icetin)@yildiz.edu.tr
b ITU, Civil Engineering Faculty, 80626 Maslak Istanbul, Turkey - fsunar@itu.edu.tr
ICWG III/VII
KEY WORDS: Coastline extraction, remote sensing, image processing, SAR, PALSAR
ABSTRACT:
Coastal management requires rapid, up-to-date, and correct information. Thus, coastal movements have primary importance for
coastal managers. For monitoring the change of shorelines, remote sensing data are some of the most important information and are
utilized for differentiating any detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline
from satellite images. In the literature most of the algorithms developed for optical images have been discussed in detail. In this
study, an algorithm which extracts coastlines efficiently and automatically by processing SAR (Synthetic Aperture Radar) satellite
images has been developed. A data set of ALOS Palsar image of Fine Beam Double (FBD) HH-HV polarized data has been used.
PALSAR image has L-band data, and has a 14 MHz bandwidth and 34.3 degrees look angle. Data were acquired in ascending
geometry. Ground resolution of PALSAR image was resampled to 15m to amplitude image. Zonguldak city, lies on the northwest
costs of Turkey, has been selected as the test area. An algorithm was developed for automatic coastline extraction from SAR images.
The algorithm is encoded in a C environment. To verify the results the algorithm was applied on two PALSAR images gathered
in two different date as 2007 and 2010. The results of automatic coastline extraction obtained from SAR images were compared to
the results derived from manual digitizing. Random control points which are seen on each image were used. The average differences
of selected points were calculated.
1. INTRODUCTION
As a peninsula country Turkey has a coastline more over than
8300 km, and 1700 km of this is surrounded by Black Sea. For
the study area coastline detection is important because this
region has mining which are located at the coast and has
international ports. Due to fact that it is needed to update
coastline information and morphological changes versus any
natural disaster. Since this area suffer from flooding and erosion
problems. It is crucial to have rapid and updatable output for
decision makers in coastal management.
Remote sensing data provide large scale surfaces to extract
changes of features. Satellite images are widely used to extract
coastline with both optical and SAR data. Wang et al (2010)
presented a class association rule algorithm and designed a
method to separate land and sea from each other. Karsli et al
(2011) developed a thresholding method using top of
atmosphere reflectance and normalized difference water index
(NDWI) of Landsat image. Moreover, ISODATA classification
method is used to observe long term shoreline changes (Yu, et
al, 2011). He indicated the significance of using archived data
for the effective assessment of coastline changes.
Wavelet based edge detection method is used for coastline
detection from ERS data by Niedermeier, et al. (2000). Liu and
Jezek, 2004, used thresholding technique on Landsat and
Radarsat data. Wang and Ellen (2008) calculated the backscatter
coefficient (dB) values of HH polarized L-band SAR data and
applied an edge-filtering model with Sobel filter. Two enhanced
Level Set Algorithm (LSA) which is based on active contours or
snakes are applied on Radarsat imagery by Ouyang et al (2010).
Another wavelet based edge detection algorithm is developed
for coastal change detection from ERS data by Chen et al.
(2011), and morphological filter is followed to refine the
boundaries.
In this study we improved an automatic coastline extraction
algorithm to extract coastline feature from ALOS/PALSAR
data. The algorithm was successfully applied on optical data
such as CORINA, IRS-1D and Landsat in previous study
(Bayram et al., 2008).
2. STUDY AREA
The study area covers the Zonguldak and Bartin cities which are
located at the coastline of the Black Sea in the north-west part
of Turkey. The adjacent provinces of the study are located as
Kastamonu at the east, Karabuk and Bolu at the south and
Duzce at the southwest. In the area there are two main streams
which drain water to the Black Sea. Bartin stream is drains at
the coast of Bartin and Filyos stream drain in Caycuma city of
Zonguldak (Figure 1).
In these cities the main economic support is underground coal
mining. It is the biggest and only hard coal mining in Turkey.
Also industrial raw materials such as limestone, marble,
quartzite are the main products of the study area.
An oceanic (maritime) climate is dominant in Zonguldak, and
precipitation is distributed almost evenly during the whole year.
The mean humidity reaches up to 9670. Along the coastline
mountain align parallel to the coast. The area has a rough
topography which rates up to 1000 m.
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