ume XXXIX-B3, 2012
MAGES
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ind Classification
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cation technique cannot
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the quality of an image;
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circles (2D) centered on
where r = 1, 3 and 5) are
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each pixel in the image
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
1.1 Local Binary Pattern (LBP)
In last two decades Local Binary Pattern has been used for
texture analysis of images including remote sensing images
[Ojala et al. 1996; Ojala et al. 2002; Lucieer et al. 2005]. The
central concept of LBP is that it measures the gray-scale
invariant texture of the image. It is derived from the analysis of
a 3 x 3 local neighborhood over a central pixel. The LBP is
based on a binary code describing the local texture pattern. This
code is built by thresholding a local neighborhood by the grey
value of its center. The eight neighbors are labeled using a
binary code {0,1} obtained by comparing their values to the
central pixel value. If the tested grey value is below the grey
value of the central pixel, then it is labeled 0, otherwise it is
assigned the value 1. The obtained value is then multiplied by
the weights given to the corresponding pixels. The weight is
given by the value 2"*!. Summing the obtained values gives the
measure of the LBP. The microwave images contain texture
and non-texture region. Advantages of using LBP on
microwave images are that (i) it can be used as a tool to
measure the spatial pattern around each pixel of the image and
(ii) it does not require any prior information about the pixel
intensity, (iii) LBP technique theoretically and computationally
is simple. This work attempts to describe the textures of
microwave images using LBP.
1.2 Objective
The aim of this study is to develop a method to classify high
resolution microwave images. To achieve this aim (i) local
adaptive median filter is developed to replace the speckle noise
of microwave images with a local median value, (ii) LBP is
used as a tool to measure the texture around each pixel of the
speckle suppressed image and (iii) ISODATA clustering
technique is used to cluster the transformed LBP image.
13 Outline of the Proposed Work
A median filter is developed to suppress the speckle noise from
microwave image. A new method is proposed to compute the
LBP of pixels in the image. This LBP analysis is used to
measure the texture around each pixel of the noise suppressed
image. To show the robustness of the proposed LBP analysis
the ISODATA is applied separately on the transformed images
obtained from Lucieer et al’s LBP analysis as well as images
obtained from the proposed LBP analysis and compared the
results. From the results, it is found that the “Lucieer et al’s
LBP analysis and ISODATA” under segment the images where
as “proposed LBP analysis and ISODATA" segment classes
distinctly. The proposed method is proved to be effective to
classify RISAT-II microwave images.
The paper is organized as follows. Section 2.0 discusses about
methodology and consists of three sub-sections. Sub-section 2.1
discusses the methods used for speckle suppression. Sub-
section 2.2 discusses the methods used for Image
Transformation. Sub-section 2.3 discusses about the clustering
of transformed image. Comparison of the result of “Lucieer et
al’s LBP analysis and ISODATA” and “Proposed LBP analysis
and ISODATA" is discussed in Section 2.4. The final
conclusions are drawn in Section 3.0.
2.0 METHODS
Methodology adapted to classify the microwave images has
three main steps: (i) suppression of speckle noise, (ii)
transformation and (iii) clustering. In the first step speckle is
identified from microwave image by using local statistics and
replaced it with a local median value. In the second step each
pixel of the image is transformed into degree of texture based
on the spatial pattern of the neighborhood. In the third step,
ISODATA clustering technique is used to cluster the
transformed image.
2.1 Speckle Suppression
A local adaptive median filter is developed to suppress the
speckle noise from microwave image. It is two step procedures:
(i) in the first step speckle is identified from microwave image
and (ii) identified speckle is suppressed in the second step. The
central pixel value that is not falling in between the range of
lower bound (LB) and upper bound (UB) value is considered as
a speckle. LB and UB are represented here as (n - o) and. (p
c) respectively, where u.. and c stand for mean and standard
deviation of the pixel values falling in the local window
respectively. The pixel value of the central pixel position of the
local window that is not falling in between LB and UB replaced
by the median (M) of the pixel values of local window.
2.2 Transformation
Transformation is carried out on the speckle-suppressed image.
The LBP is used as a tool to transform the image. Intersected
pixels by the perimeter of the circles are used for computing the
LBP. The proposed method uses a series of circles (2D)
centered on the pixel with incremental radius values. Measures
rotation invariant texture for each circularly symmetric
neighborhood. Finally all measure together becomes LBP value
of the center pixel of the kernel. The significance of uses of a
series of circles is that (i) the circular neighborhoods enable a
definition of a rotation invariant texture and (ii) multiple circles
facilitate to consider more number of pixels than a circle to
compute the degree of texture of the neighbor of the center
pixel of the kernel. Detail procedure to compute the degree of
texture of the neighborhood of the pixel is given bellow:
Initially for each pixel a series of three values of radius r (where
r = 1, 3 and 5) are considered as shown in figure 1 for
measuring the degree of texture around the pixel.
The grey values { fo, fi, ............. A 1 S90 81: ;
gp.1 ) and { hg by, ............. , hps.; } of intersected pixels on
the perimeter of the circle of radius r =I, r =3 and r =5
respectively are used for measuring the same. Here, P, (P; > 1),
P, (P,> 1) and P; (P; > 1) are the total number of intersected
pixels on the perimeter of the circle of radius r=1,r=3 and r =5
