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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
AUTOMATIC EXTRACTION OF WATER IN HIGH-RESOLUTION SAR IMAGES
BASED ON MULTI-SCALE LEVEL SET METHOD AND OTSU ALGORITHM
HG. Sui* C. Xu^*
^ State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,
Wuhan, 430079, PR China, xc992002@foxmail.com
Commission VII, WG VII/7
KEY WORDS: Synthetic aperture radar (SAR), Multi-scale level set, Water extraction, OTSU algorithm, Segmentation
ABSTRACT:
Water extraction has an important significance in flood disaster management and environmental monitoring. Compared to optical
sensor, Synthetic aperture radar (SAR), which has the properties of high resolution and all-weather acquisition, has been used for
water extraction in this paper. Due to the presence of coherent speckles, which can be modeled as strong, multiplicative noise, water
extraction in SAR image is very difficult. In order to extract water from SAR images automatically, accurately and quickly, a novel
water extraction algorithm combine multi-scale level set method with OTSU algorithm is proposed in this paper. Firstly, we
introduced multi-scale framework into level set method. Multi-scale framework is a method considering both global information and
local information of the image. The overall structural information of the image can be maintained at coarse scales and detailed
information can be kept at fine scales. Therefore, coarser scale extraction results can be used as a prior guide for the finer scale, so
that not only are the statistical properties of the signal-resolution image considered, but also statistical variations of multiple
resolutions are exploited. Moreover, computational complexity is reduced since much of the work can be accomplished at coarse
resolutions, where there are significantly fewer pixels to process. Secondly, based on the multi-scale level set framework, the
segmentation result of OTSU algorithm is used to represent the initial segmentation curve. Finally, in order to eliminate the influence
of buildings shadow and road, post-processing is considered in this paper. The experiments with real SAR images demonstrate the
effectiveness of the new method.
1. INTRODUCTION
Synthetic Aperture Radar (SAR) has the capability of large-area
coverage, cloud penetration and all-weather acquisition, and it
can usually obtain massive information within a short time.
Thus, they are more suitable than optical sensors to reliably and
timely map inundated areas in flood situations, which usually
occur under overcast sky conditions. The high resolution and
the increased observation frequency of the new class of SAR
sensors offer enormous potential in the domain of flood
mapping. However, the improved spatial resolution of the SAR
data results in a large variety of very small-scaled image objects,
which makes image processing and analysis even more
challenging.
Segmentation is the main tool to extract water bodies from SAR
images. However, there exist some difficulties: On the one hand,
due to the presence of coherent speckles, which can be modeled
as strong, multiplicative noise, segmentation of SAR images is
generally acknowledged as a difficult problem; see Lee (1989)
and Oliver and Quegan (1998). On the other hand, confused
objects may influence the result of segmentation, such as
buildings shadow and road.
The level set method was first introduced by Osher and Sethian
(1988), and since then, much effort has been directed towards
image segmentation (for example: Mumford and Shah 1989,
Zhao et al. 1996, Horritt 1999, Germain and Refregier 2001, Li
et al. 2005, Law et al. 2008). Compared with some other SAR
image segmentation methods (for example: Cook et al. 1994,
* Corresponding author. E-mail address: xc992002@ foxmail.com
Fjortoft et al. 1998, Xu et al. 2003), the level set method has
the advantages of being robust in locating the boundary of an
object, and of being able to handle topological changes in the
curves during their evolution.
The most general model in the level set method is the Chan-
Vese (C-V) model (Chan and Vese 2001), which is based on
curve evolution techniques employing the Mumford-Shah
functional (Mumford and Shah 1989) for segmentation, and
level sets. This model can detect objects whose boundaries are
not necessarily defined by a gradient and can be adapted more
easily to topological changes. Although traditional level set
methods based on the C-V model have obtained encouraging
results, prior information (speckle noise) of SAR images is
commonly ignored. It is widely recognized that the gamma
distribution is the most general model employed to represent a
SAR image, thus, many authors employ the gamma statistical
model instead of the C-V model to define the energy functional.
For example, Martin et al. (2004) analyzed the level set
implementation of region snakes based on the maximum
likelihood method for different noise models, and obtained
improved segmentation results. Ayed et al. (2005) investigated
SAR image segmentation into a given but arbitrary number of
gamma homogeneous regions via active contours and level sets.
Silveira and Heleno (2009) adopted a mixture of lognormal
densities for SAR image segmentation between water and land,
and results demonstrated the good performance of their
proposed method.
