A Novel Coastline Detection Algorithm Based On Modified Iterative Selection Method

Hongyu Chen1,a, XiaoFei Shi*,1, 2 , b ,Lei Feng1,c,Yuelong Zhang1,d,Yanhua Li1,e 1Information Science and Technology College, Dalian Maritime University. Dalian,116026,China

2National Marine Environmental Monitoring Center, Dalian, 116023 , China

aemail:chuckchy0819@126.com, bemail: shi_xiao_fei_57@163.com, cemail: fleissig367@126.com,

demail: dlmzyl123@163.com, eemail: 1395324372@qq.com *Corresponding author: Xiaofei Shi, Email: shi_xiao_fei_57@163.com

Keywords: Modified Iterative Selection; Image Segmentation; Coastline Extraction

Abstract. Misjudgment often occurred in low contrast remote sensing images, because most widely

used image segmentation algorithms often have a larger threshold. To overcome this problem, a

novel coastline detection algorithm is proposed. A restriction function is involved into conventional

iterative selection process. According to langrage multiplier, a modified iterative selection model is

formulated. This modified method utilizes the gradient of images to obtain an optimal threshold. A

region grouping rule is proposed to distinguish land and sea. Experimental results show superior

performance of proposed method in terms of accuracy. As an application, our method has been

applied to extract the coastline of the remote sensing image with promising results.

Introduction

A coastline is defined as a boundary between sea and land masses [1]. Knowledge of coastline is

the basis for characterizing and measuring the sea and land resources, and it is critical for

autonomous navigation, geographical exploration, coastline resource management, coastal

environmental protection [2][3]. There is an increasing interest in coastline extraction.

Remote sensing technology is particularly suited to detect the coastline because it can easily

capture features, on Earth’s surface, in large area [4]. Conventionally, coastline was manually

traced by cartographers. Due to the subjectivity and substantial effort involved in manual

delineation, in the past decades, a great deal of research effort has been devoted to coastline

extraction from satellite imagery.

Coastline extraction methods can be divided into four categories: Threshold method [2][5],edge

detection method [6][7][8], clustering method [9] and active contour method [3][10]. Compared

with the other three categories, the threshold method is more visible, simple and convenient.

Threshold method is based on gray value histogram. In this category, all pixels are classified into

two groups, according to a threshold which is determined based on some special criteria [5], such as

maximum entropy [11], maximum variance between classes in OTSU [12], minimum error function

in iteration selective method [13]. But misjudgment between the two groups often occurs, since an

inappropriate threshold is chosen.

In this paper, a modified iterative selection method is proposed to detect coastline. In

conventional iterative selection method, the threshold is a bit larger in low contrast remote sensing

images. To overcome this problem, A restriction function is involved into iterative selection process.

According to langrage multiplier, a modified iterative selection model is formulated. This modified

method utilizes the gradient of images to obtain an optimal threshold. The proposed method

demonstrates better performance in contrast with conventional methods.

A Modified Iteration Selective Method

At present, the traditional iteration selective method [14] assumes to find a threshold T and

then create a binary image in which all pixels having a darker gray level than T will be replaced

Applied Mechanics and Materials Vols. 602-605 (2014) pp 1864-1867 Submitted: 31.05.2014Online available since 2014/Aug/11 at www.scientific.net Accepted: 11.06.2014© (2014) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMM.602-605.1864

All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP,www.ttp.net. (ID: 130.194.20.173, Monash University Library, Clayton, Australia-07/12/14,03:41:33)

by the gray level A ,and other brighter than T will be replaced by B . And then define an error

function 2e as (1)

2 2 2

0( ) ( ) ( ) ( )

T N

Te i A h i di i B h i di= − + −∫ ∫ (1)

where i indicate the gray level (varying from 0 to N ), T is the threshold ， ( )h i is the

histogram weighting for every gray level，and ,A B is the sample mean values of each of the two

components.

Tradition threshold method always achieves a higher threshold in the segmentation of the

gradient image. In order to gain an appropriate threshold, we propose a new error function 2e

2 2 2 2

0( ) ( ) ( )

T N

Te i h i di i M h i di Mλ= + − +∫ ∫ (2)

where i indicate the gray level (varying from 0 to N ), T is the threshold, ( )h i is the histogram

weighting for every gray level. M is the sample mean values which brighter than T , and λ is

weighting positive constant.

To minimizes the error function 2e , we derive the error function relative to T and equate the

result to zero: 2

2 2( ) ( ) ( ) 0e

T h T T M h TT

∂= − − =

∂ (3)

And then we obtain 2 2( ) 0T T M− − = (4)

Or in other words

2

MT = (5)

Next utilizing the same way to solveM , we derive the error function relative to M and equate

the result to zero: 2

2 ( ) ( ) 2 2 ( ) 2 ( ) 2 0N N N

T T T

ei M h i di M ih i di Mh i di M

Mλ λ

∂= − − + = − + + =

∂∫ ∫ ∫ (6)

We can obtain

( ) ( ) 0N N

T TMh i di M ih i diλ+ − =∫ ∫ (7)

In other words

( )

( )

N

T

N

T

ih iM

h i λ

=

+

∫

∫ (8)

In the following section, the proposed method is utilized to detect the coastline in low contrast

remote sensing images.

A Novel Coastline Detection Algorithm Based on Modified Iterative Selection Method

Our coastline extraction method consists of four stages: pre-segmentation, segmentation,

post-segmentation and coastline extraction. In pre-segmentation stage we achieve the

morphological gradient of original image, Then in segmentation stage, the gradient image is

partitioned using a threshold by our modified iterative selection method. In post-segmentation, an

adaptive region grouping algorithm is utilized to partition image into homogeneous land and water

regions. Finally, an edge detection algorithm is used to extract the coastline.

To gain the precise coastline, morphological gradient image should be required. The

morphological gradient f of an image is set as:

f A B A B= ⊕ − Θ (9)

where A is the original image, and B is the morphological factor A B⊕ means dilatation and

erosion is defined as A BΘ .

Applied Mechanics and Materials Vols. 602-605 1865

The coastline detection algorithm based on modified iterative selection method is described

briefly as follows:

a) Select the initial estimate 0

1(max( ))

2T f= , where max( )f is the maximum value of the

morphological gradient of original image.

b) Compare the gradient value with T , and calculate the value M by (8)

c) Calculate the new T by (5)

d) Repeat the (b) ~ (c) steps until the variation T is less than 1

An adaptive region grouping rule is proposed to group land and water region. We label all pixels

by four- or eight-neighborhoods of current pixel. Each region is labeled by a unique identification

number characterized by itsarea . An adaptive region grouping rule is defined as follows:

max

max

1

0

area areaarea

area area

<=

≥

，

，

(10)

where max 1 2max( , )area area area=

After region grouping, coastline can be extracted by any edge detector such as Canny.

Experimental results

The remote sensing image used in this experiment was acquired from Nation Marine

Environmental Monitoring Center. By applying our method to the image, we have successfully

extracted a complete coastline. Original image is show in Figure1 (a) into matlab. The

morphological gradient image is demonstrated in Figure1 (b), Figure1 (c) is the Otsu segmentation

result. Figure1 (d) is the segmentation result of traditional iteration selective method. Figure1 (e) is

the result of our modified iterative selection method segmentation result with =0.1λ . The binary

image is shown in Figure1 (f),and Figure1 (g) is the final coastline detection result.

(a) original image (b) gradient image (c) Otsu method (d) Iterative selective

(e) modified method (f) binary image (g) result

Fig.1. Experimental results of our proposed method compared with Otsu and Iterative selective

method

Compared with the other two methods, the experiment shows that our modified iterative

selection method can gain a better result with a lower threshold. A perfect coastline can be obtained

by our proposed algorithm.

Conclusion

We have presented a modified iterative selection method to segment low contrast remote

sensing images. Based on the traditional iterative selection method, we defined a modified error

function and segmentation estimation is performed by minimizing the modified error function.

Experimental results have demonstrated superior performance of our method in terms of accuracy.

1866 Advanced Manufacturing and Information Engineering, IntelligentInstrumentation and Industry Development

As an application, our method has been applied to extract the coastline of the coastline with

promising results.

Acknowledgement

In this paper, this research was supported by the fund of Key Laboratory of marine

management technology in State Oceanic Administration People’s Republic of China (201311).

References

[1] Nunziata F, Li X, Ding X. Coastline Extraction Using Dual-Polarimetric COSMO-SkyMed

PingPong Mode SAR Data[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(1):104-108.

[2] Liu H, Jezek K C. Automated extraction of coastline from satellite imagery by integrating

Canny edge detection and locally adaptive thresholding methods[J]. International Journal of Remote

Sensing, 2004, 25(5): 937-958.

[3] Sheng G, Yang W, Deng X, et al. Coastline detection in synthetic aperture radar (SAR) images

by integrating watershed transformation and controllable gradient vector flow (GVF) snake

model[J]. IEEE Journal of Oceanic Engineering, 2012, 37(3): 375-383.

[4] Niedermeier A, Romaneessen E, Lehner S. Detection of coastlines in SAR images using wavelet

methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2270-2281.

[5] You X, Li W. A sea-land segmentation scheme based on statistical model of sea[C], 4th IEEE

International Congress on Image and Signal Processing (CISP), 2011, 3: 1155-1159.

[6] Xu-kai Z, Qiong-qiong L, Baig A, et al. Automated detection of coastline using Landsat TM

based on water index and edge detection methods[C]. IEEE Second International Workshop on

Earth Observation and Remote Sensing Applications (EORSA), 2012: 153-156.

[7] Zhang R, Fang D G, Wu K L, et al. Study on the elimination of surface wave by metal fences[C].

IEEE Proceedings of Asia-Pacific Conference on Environmental Electromagnetics, 2000: 174-178.

[8] Karantzalos K G, Argialas D, Georgopoulos A. Towards automatic detection of coastlines from

satellite imagery[C]. Proc. IEEE Int. Conf. Digital Signal Process. 2002, 2: 897-900.

[9] Sjahputera O, Scott G J, Claywell B, et al. Clustering of detected changes in high-resolution

satellite imagery using a stabilized competitive agglomeration algorithm[J]. IEEE Transactions on

Geoscience and Remote Sensing , 2011, 49(12): 4687-4703.

[10] Silveira M, Heleno S. Separation between water and land in SAR images using region-based

level sets[J]. IEEE Geoscience and Remote Sensing Letters , 2009, 6(3): 471-475.

[11] Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using

the entropy of the histogram[J]. Computer vision, graphics, and image processing, 1985, 29(3):

273-285.

[12] Otsu N. A threshold selection method from gray-level histograms[J]. Automatica, 1975,

11(285-296): 23-27.

[13] Ridler T W, Calvard S. Picture thresholding using an iterative selection method[J]. IEEE

transactions on Systems, Man and Cybernetics, 1978, 8(8): 630-632.

[14] Magid A, Rotman S R, Weiss A M. Comments on picture thresholding using an iterative

selection method[J]. IEEE Transactions on Systems, Man and Cybernetics, 1990, 20(5): 1238-1239.

Applied Mechanics and Materials Vols. 602-605 1867

Advanced Manufacturing and Information Engineering, Intelligent Instrumentation and Industry

Development 10.4028/www.scientific.net/AMM.602-605 A Novel Coastline Detection Algorithm Based on Modified Iterative Selection Method 10.4028/www.scientific.net/AMM.602-605.1864

DOI References

[1] Nunziata F, Li X, Ding X. Coastline Extraction Using Dual-Polarimetric COSMO-SkyMed PingPong

Mode SAR Data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 104-108.

http://dx.doi.org/10.1109/LGRS.2013.2247561 [2] Liu H, Jezek K C. Automated extraction of coastline from satellite imagery by integrating Canny edge

detection and locally adaptive thresholding methods[J]. International Journal of Remote Sensing, 2004, 25(5):

937-958.

http://dx.doi.org/10.1080/0143116031000139890 [3] Sheng G, Yang W, Deng X, et al. Coastline detection in synthetic aperture radar (SAR) images by

integrating watershed transformation and controllable gradient vector flow (GVF) snake model[J]. IEEE

Journal of Oceanic Engineering, 2012, 37(3): 375-383.

http://dx.doi.org/10.1109/JOE.2012.2191998 [4] Niedermeier A, Romaneessen E, Lehner S. Detection of coastlines in SAR images using wavelet

methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2270-2281.

http://dx.doi.org/10.1109/36.868884 [9] Sjahputera O, Scott G J, Claywell B, et al. Clustering of detected changes in high-resolution satellite

imagery using a stabilized competitive agglomeration algorithm[J]. IEEE Transactions on Geoscience and

Remote Sensing , 2011, 49(12): 4687-4703.

http://dx.doi.org/10.1109/TGRS.2011.2152847 [10] Silveira M, Heleno S. Separation between water and land in SAR images using region-based level

sets[J]. IEEE Geoscience and Remote Sensing Letters , 2009, 6(3): 471-475.

http://dx.doi.org/10.1109/LGRS.2009.2017283 [11] Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the

entropy of the histogram[J]. Computer vision, graphics, and image processing, 1985, 29(3): 273-285.

http://dx.doi.org/10.1016/0734-189X(85)90125-2 [13] Ridler T W, Calvard S. Picture thresholding using an iterative selection method[J]. IEEE transactions on

Systems, Man and Cybernetics, 1978, 8(8): 630-632.

http://dx.doi.org/10.1109/TSMC.1978.4310039 [14] Magid A, Rotman S R, Weiss A M. Comments on picture thresholding using an iterative selection

method[J]. IEEE Transactions on Systems, Man and Cybernetics, 1990, 20(5): 1238-1239.

http://dx.doi.org/10.1109/21.59988