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RESEARCH ON EDGE EXTRACTION WITH LEVEL SET METHOD 
Zhang Junru a , Jiang Xuezhong a * 
a State Key laboratory of Estuarine and Coastal Research, 200062, East China Normal University,ShangHai - 
zjunru@gmail.com, xzjiang@sklec.ecnu.edu.cn 
Commission IV, WG IV/3 
KEY WORDS: Remote Sensing, Image processing, Edge Extraction, Level Set, C-V model 
ABSTRACT: 
Image edge detection or extraction is always a research hotspot in remote sensing image processing. Up to now, there are many 
methods have been used in this area, such as threshold segmentation, edge detection operators, region growing method and so on. But 
threshold segmentation cannot meet the accuracy we needed and edge detection operators are always extract all the edges in the 
image when sometimes we just need part of them. In this paper, we proposed using level set method to extraction edges that we want. 
At first, initial edge was given by Human-Computer Interaction way, and then level set method was used to evolve the initial edge to 
make it more accurate. Experiments were carried out in Matlab, and the results show that this method is very useful and high 
efficiency. 
1. INTRODUCTION 
Ostensibly, edge is the junction of different features in remote 
sensing image. It usually is the places where image brightness 
changes most in the local part of an image. Edge exists 
primarily on places between one type of objective and the other, 
objectives and backgrounds, region and region, and also is basis 
of texture feature extraction, shape feature extraction and image 
analysis. Edge extraction is the first step of image analysis and 
understanding, occupies a special position in the image 
processing and computer vision, and it is the one of the most 
important aspects of low-level processing. In the field of remote 
sensing image processing, edge detection has a wide range of 
applications, many research achievements have been made in 
such as road extraction, waterline extraction, cloud detection, 
remote sensing image segmentation. 
Because of the importance of edge extraction, lots of methods 
have been proposed by researchers, they can be divided into a 
few major categories: 1. Threshold segmentation(Xie fengying, 
Jiang Zhiguo, 2007; Li Jiangtao, Ni Guoqiang, Huang 
Guanghua, 2007; R.Medina-Camicer, F.J.Madrid-Cuevas, 2008; 
Ety Navon, Ofer Miller, Amir Arerbuch, 2005); 2. Method of 
differential operator based on gradient(Phillip A. Mlsna, Jeffrey 
J. Rodriguez, 2005; Lijun Ding, Ardeshir Goshtasby, 2001); 3. 
Region growing method(Jiangping Fan, Guihua Zeng, Mathurin 
Body,Mohand-Said Hacid, 2005; Ye zhou,Jhon Starkey, Lalu, 
2004);4. Method of Mathematical Morphology(T.Chen, 
Q.H.Wu, R.Rahmani-torkaman, J.Hughes, 2002;); 5. Wavelet 
Methods(Dusan Heric,Damjan Zazula, 2007; Ming-Yu,Shih, 
Din-Chang Tseng, 2005); 6. Method of Fuzzy 
Mathematics(Sakari Murtovaara, Esko Juuso, Raimo Sutinen, 
1996; Florence Jacquey, Frédéric Comby, Olivier Strauss, 
2008; Liming Hu, H.D. Cheng, MingZhang, 2007); and 7. 
Method of neural networks(V.Srinivasan, P.Bhatia, S.h. Ong, 
1994; M.Emin Yuksel, 2007). Sometimes, a few of these 
methods are combined together to perform edge extraction. 
But there are some problems still exist; first. It is hard to 
determine thresholds, though many methods are proposed like 
neural networks to help to do this, there are still many mistakes 
in local area of an image due to the value always determined by 
the whole image. Second, sometimes we just want to extract a 
few edges like the road, waterline and so on, but many edge 
detectors perform on the whole image, so a lot of tasks need to 
do after using these edge detectors. Here we proposed a method 
to extract edges based on level set method, on one way, we try 
to provide a friendly Human-Computer Interactive environment 
to perform this task automatically or semi- automatically; on 
another way, we hope that after using this method, the 
follow-up processing tasks would be very easy. 
2. LEVEL SET METHOD 
2.1 Fundamental of level set method 
Level set method was proposed by Osher and Sethian in 1988 
and has been widely used in image segmentation, image 
smoothing, motion segmentation, moving target tracking, even 
in stereovision and image reparation. As a novel approach of 
handling the curve evolution, it implied in a manner to express 
the closed planar curves or three-dimensional closed surface, so 
as to avoid the evolution process of tracing the curve. Thus 
problem of curve evolution is converted to a pure searching for 
the numerical answers of partial differential equation. 
The main idea of level set method is to embed the propagating 
interface as the zero level set of a higher dimensional 
hypersurface. By controlling the evolution of the hypersurface, 
we can control the evolution of the curve. 
Suppose the level set function is 0(x y t , C(p, t) is the zero 
level set of C'Xyl , then, we produce an Eulerian 
formulation for the motion of the hypersurface propagating 
along its normal direction with speed V, where V can be a 
function of various arguments, including the curvature, normal 
direction, etc. Let C(p, t=0) is the initial curve, ©fxt = 0) is 
usually defined by SDF (Signed distance function): 
Cxi = C = Hbd(x)