water/land segmentation for sar images based on … · geodesic distance, combining the manifold...

WATER/LAND SEGMENTATION FOR SAR IMAGES BASED ON GEODESIC DISTANCE

Hua Zhong, Qing Xie, L.C. Jiao, Shuang Wang27 July 2011

Key Laboratory of Intelligent Perception and Image Understandingof Ministry of Education of China, Xidian University,

Xi'an 710071, P.R. China

OutlinesOutlines

1

2

3

4

Research Background

Geodesic Distance

The Proposed Method

Experimental Results

Research BackgroundResearch Background

Characteristics of SAR

① Advantage: working without solar illumination and in all weather conditions, compared to satellite optical images.

Many applications:

For instance, imaging the Earth surface, environmental monitoring, target detection ( coastline, bridges, etc ).


Characteristics of SAR

② Disadvantages: SAR is affected by multiplicative speckle,

gives the images a grainy appearance

makes the interpretation of SAR images a challenging task


Why focus on water/land segmentation in SAR image?

An important application

Water/land separation in synthetic aperture radar (SAR) images is an increasingly used tool in environmental monitoring applications such as flood extent mapping or coastline extraction.


Characteristics of Water/Land segmentation

Water/land separation is a particular case of SAR image classification, with only two classes to assign. Water surfaces:

behave as specular reflectors at radar wavelengthsappears as low intensity areas in SAR images

Land:Comparatively brighterthe rougher surrounding terraincharacterized by diffuse scattering.

The job seems easy, however, difficult, in fact, because of the influences of the depth, the complexity of water/land boundary, and the wide range of land intensities. Also the multiplicative speckle SAR images increases the difficulties，and it is difficult to reach a precise segmentation.

FrameworkFramework

Our method includes two steps: Coarse segmentation

roughly segments between water/land regions only based on the probability models for speedRefinement.

refines only the boundary areas using geodesic distance with automatically generated class labels and adaptively determined bandwidth.

We only applies the distance computation in the refine step, because the computation of geodesic distance costs most of the runtime, and the precise outlines is expected especially in those tiny details such as complex shaped shorelines, bridges and quay shipside. Boundary area with adaptive bandwidth can further accelerate the segmentation..

FrameworkFramework

What we have done?a novel method for water/land segmentation is proposed based on the framework of geodesic distance.

1. Water/land modelingaccording to the statistics of both the speckle and land covers,which leads to a fast point-wised coarse segmentation

2. Boundary area with adaptive bandwidth Based on the water/land models, the boundary area between water and land can be localized with automatically generated class labels and adaptively determined bandwidth

3. Improved geodesic distance Then the refined segmentation is implemented using an improved geodesic distance, combining the manifold idea to enlarge the inter-class differences.

Briefly review ------ Geodesic Distance

Let and be the label set for object and background, respectively. The geodesic distance is simply the smallest integral of a weight function over all possible paths from the labels to any pixel . Specifically, the distance from each of the two classes of labels to any pixel is defined as

where

where is weight function for pixel , and is the path connects any two pixels and [4].

[4] X. Bai, G. Sapiro, “Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting,” Int. J. Comput Vis., vol. 82, pp. 113-132, 2009.

Geodesic DistanceGeodesic Distance

OΩ BΩ( )d x

x

( ) min ( , ), { , }ll s O BD x d s x l∈Ω= ∈ Ω Ω

2

, 1 21 2 11 2 ,( , ) min ( ) ( )

s s

s

C i s s i isd s s Y x C x dx= ⋅∫

( )iY x ix1 2,S SC

1s 2s

1. Water/land models

The labels for both classes can be obtained offline:

: the water, assumed to follow the gamma distribution

- the land, modeled as Gaussian mixed model (GMM) , representing the widely spread intensity range of land cover.

The Proposed MethodThe Proposed Method

LΩ

WΩ


For the water label set :

The probability distribution model is used according to gamma distribution,

where is intensity of a pixel, is the equivalent noise level (ENL)

of , is the mean of pixel intensities in .


( )WF v

1

( ) exp( )( 1)!

N N

W N

N v N vF vN I I

−⋅ ⋅= −

−

v NWΩ I WΩ

WΩ


For the land label set :

we construct as followed:

,

where and .

The mean and the std can be obtained directly through the set

The parameters and are computed from the subset , which includes the pixels with intensities larger than a threshold.

The weights and are used to balance the role of and .


LΩ

( )LF v

1 1 2 2( ) ( ) ( )LF v k f v k f v= ⋅ + ⋅

21 1 1( ) ~ ( , )f v N μ σ 2

2 2 2( ) ~ ( , )f v N μ σ

1σ LΩ1μSΩ2μ 2σ

1k 2k1( )f v 2 ( )f v

1 2 1k k+ =

2. Boundary area with Adaptive bandwidth

Given the boundary (which can be obtained from the coarse segmentation, a sliding window is taken along with the center pixel denoted as and the radius as .

Intuitively, the bandwidth depends on two factors:

1. boundary variance

2. smoothness


∂Ω( )N x ∂Ω

x ( )R x

2Bσ

L∇


The boundary variance is defined as

which means how much the boundary can be distinguished, and helps to control the width of the current window.

is the weight for each pixel

is the Euclidean distance between pixel and the boundary .

is the weighted distance, presented as:


( )w y

2Bσ

2

( )2

( )

( )( ( ) ( ))( )

( )

E Ey N x

B

y N x

w y d y d xx

w yσ ∈

∈

−=∑

∑

( )Ed y y ∂Ω( )Ed x

( )

( )

( ) ( )( )

( )

Ey N x

E

y N x

w y d yd x

w y∈

∈

=∑∑

y


The smoothness factor reflects is defined as

where is the length of within the window .

the smaller is, the smoother the boundary is.


L∇

∂Ω

( ) ( ) 2 ( )L x L x R x∇ = −

( )L x ∂Ω ( )N xL∇


Finally, the adaptive bandwidth with the radius is obtained as

And the sliding windows along with the radius consist the

adaptive boundary area .


* ( )R x

* ( ) max{ ( ), ( ) ( )}E BR x L x d x xσ= ∇ +

L∂Ω * ( )R x

beltΩ


3. Improved geodesic distance

Inspired by the idea of manifold , the distance can be approximated by adding up a sequence of “short hops” between neighboring points.

Let denotes the new weight function, a factor is introduced in order to amplify the between-class distance and minify the within-class difference. is defined as

It is obviously that through we can easily amplify the difference range from to . Replacing the weight with , we can get the improved geodesic distance .

'( )iY x ρ

'( )iY x( )'( ) l iF x

iY x ρ∇=

ρ[ ]0,1 [ ]1,ρ ( )iY x '( )iY x

( )lD x

FrameworkFramework

The details: 1. Establish the models and for the water/land, respectively, as

described in section 3.1; 2. Coarse Segmentation: For each pixel , its probabilities belong to

water and land are computed according to the models, and then coarsely segmented.

3. The coarse boundary could be acquired based on the coarse segmentation result.

4. Locate the adaptive boundary area, as described in section 3.2; At each sliding window on , the pixels whose likelihood rank thelargest ones are selected as the automatically generated labels for each class.

5. Refinement: perform the improved geodesic distance in the boundary area to get the refined boundary.

Fig.1. Step results of the proposed method. (a) Real SAR image; (b) Coarse Segmentation; (c) Automatically generated labels (point labels for land and lines for water); (d) Boundaryarea with adaptive bandwidth; (e) Geodesic distance to water labels (brighter pixels meansthe shorter distance); (f) Refined results.

ExperimentsExperiments

(a) (b) (c)

(d) (e) (f)


Fig. 1(b) shows coarse segmentation of Fig. 1(a) fast using only the probability modelslocalization of initial boundary area very well the automatically generated labels (Fig. 1(c)) are correct due to

the effectiveness of our probability models. Fig 1(d)-(f) show the process of refinement.

the boundary area with adaptive bandwidth in Fig.1(d) can reflect well the complexness of the boundary. ( the area around the bridges and quays has larger harbor

bandwidth, and the area along the riverside is smaller in comparison)Fig. 1(e) gives the map of the geodesic distance( the difference between water and land is very clear. )

in Fig 1(f), the tiny details of the bridges and the quays are better maintained.

Fig.2. Some example images (first line) and the segmentation results (second line)



Fig. 2 presents some results on the other real SAR images.

as a whole, the details are also very clear.from Fig. 2(a), we can see that though the land background is

complex, and the contour of water body is complicated with many tiny details, the proposed method accurately segments water from land regions, with the important details well maintained such as the shape of riverside and bridges, including a very thin one, which is difficult to be distinguished by human eyes. our segmentation result shows high interior uniformity in both

land area and water part. fig. 2(b) and (c) show images with quays, which locate very

close and seem hard to segment. In addition, each quay has different contour but looks similar. Our method accurately maintains the clear shape of each quay and the tiny space between quays.


the linear complexity Our method has the linear complexity due to the use of

geodesic distance.On the other side, we only apply the distance computation in

the refine step to further reduce computation complexity.Also, boundary area with adaptive bandwidth can further

accelerate the segmentation. for example, the actual run time on Fig.2(c) ( with the size 190×190)

with a Matlab implementation on a Pentium 2.7GHz CPU– Our method with adaptivity boundary bandwidth: 18.18 seconds – Our method with fixed boundary bandwidth: 35.57 seconds

when applying geodesic distance for both the coarse segmentationand the refinement,

the run time increases rapidly and costs 690 seconds.

REFERENCES REFERENCES

[1] J.B. Henry, P. Chastanet, K. Fellah, and Y.L. Desnos, “Envisatmultipolarized ASAR data for flood mapping,” Int. J. Remote Sens., vol. 27, pp. 1921–1929, May 2006.

[2] A. Niedermeier, D. Hoja, and S. Lehner, “Topography and morphodynamics in the german bight using SAR and optical remote sensing data,” Ocean Dynamics, vol. 55, pp. 100–109, 2005.

[3] M Silveira, S Heleno, “Water/Land Segmentation in SAR Images using Level Sets”, in Proc. ICIP 2008, San Diego, California, October 2008.

[4] X. Bai, G. Sapiro, “Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting,” Int. J. Comput Vis., vol. 82, pp. 113-132, 2009.

[5] J. B. Tenenbaum, V. de Sliva, J. C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, no. 5500, pp. 2319-2323, 2000.

[6] J. S. Lee, J. H. Wen, T. L. Ainsworth, K. S. Chen, and A. J. Chen. Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans. Geosci. Remote Sens., vol. 47, no. 1, pp. 202-213, Jan. 2009.

[7] Sandia National Labroaries. http://www.sandia.gov.

water/land segmentation for sar images based on … · geodesic distance, combining the manifold...

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