=Paper=
{{Paper
|id=Vol-2076/paper-07
|storemode=property
|title=Linear Objects Detection on SAR Images
|pdfUrl=https://ceur-ws.org/Vol-2076/paper-07.pdf
|volume=Vol-2076
|authors=Oleg Yu. Ivanov
}}
==Linear Objects Detection on SAR Images==
https://ceur-ws.org/Vol-2076/paper-07.pdf
Linear Objects Detection on SAR Images
Oleg Yu. Ivanov
Ural Federal University, Yekaterinburg, Mira st., 19, Russia,
ol iv@list.ru
Abstract. Allocation of linear structures on images is required to solve
a large number of thematic problems of the Earth remote sensing. Roads
and railways, power lines, pipelines, and borders of natural areas “land-
sea”, “forest-field”) are examples of such structures. It is known that
traditional algorithms for linear targets and structures detection are
not effective, because radar images have special distinctive features (i.e.
speckle-noise), which complicate the target detection problems. In the
paper neural network algorithm based on the Hough transform and ap-
plication of Kohonen neural networks is suggested and researched. At the
first stage, a radar image is transformed into a Hough plane, where linear
targets give peak responses; then a Kohonen neural network is used to
find these peaks. It is shown that the neural gas algorithm for the network
weights adjustment is more suitable than the “Winner takes all” rule.
Also, an exponential weight calibration function for better convergence is
offered. Examples of real radar space images processing obtained by the
RADARSAT-1 are given. Results of processing show that the suggested
algorithm is suitable for linear targets detection on the radar images.
Keywords: Linear objects detection algorithm, synthetic aperture radar,
Hough transformation, Kohonen neural network
1 Introduction
Nowadays satellite imagery becomes increasingly popular due to the wide spec-
trum of solved problems. Successful operation with a variety of orbital remote
monitoring systems allow one to obtain quality images of the Earth surface in
different bands of the electromagnetic spectrum. Among the latter, the synthetic
aperture radar (SAR) technology plays an important role and it can carry out
imagery regardless of weather conditions and natural lighting of surface [1, 6, 7].
There are several algorithms for automatic linear structures detection on the
remote sensing data, and functional analysis (Fourier analysis, Gabor wavelets
algorithm) and parametric analysis algorithms [2] are most commonly used.
The problem becomes more complicated by the fact that coherent radar images
(obtained by SAR) have their own very specific characteristics, and the most
important among them is their distinctive spotting, so-called the speckle-noise
[1]. Speckle significantly reduces effectiveness of the mentioned algorithms. In
this paper, we consider the algorithm of linear structures detection based on the
Hough transformation with subsequent analysis with on the basis of Kohonen
59
neural network. Application of the neural networks gives one a possibility of
parallel processing and provides high noise immunity.
2 Classification algorithm
The proposed algorithm consists of several steps. At first, the Hough transforma-
tion is performed on a fragment of the original image I(m, n), which transforms
a pixel coordinates (m, n) into a Hough space A(ρ, θ)
ρ = m cos θ + n sin θ.
where m and n are the point pixel Cartesian coordinates, and ρ and θ are the
point polar coordinates.
As a result of this transformation, each line of the original image will corre-
spond to a point in the Hough space (Fig. 1) For a binary image, the transforma-
tion is performed for non-zero pixels only. For a gray imagery, the transformation
result values in the Hough plane are multiplied on the pixel value [4]. The opti-
mal sampling interval of the parameter ρ is equal to one, and the parameter θ
sampling each interval should not exceed 0.02π radians. Choice of the origin at
the center of original image also helps to reduce errors in subsequent calculations.
Further, the image in the Hough plane is converted into an array of training
vectors for a neural network. The easiest way is to match the value pair (ρ, θ) on
the Hough plane with vectors x, whose number for this cell will be proportional
or equal to the A(ρ, θ) value for this cell. To improve convergence of the learning
algorithm, it is better to take such number of vectors that will be proportional
to (A(ρ, θ))q , q = 1.5 . . . 2.0 (Fig. 2, 3) [4].
The next step consists in normalization of vectors; this is performed by the
formulas
x1 = 0.7(2Nang θi − 1)/(Nang /Nnorm ),
x2 = 0.7(2Nnorm ρi − 1),
x23 = 1 − x21 − x22 ,
where Nnorm and Nang are the sizes of the Hough plane (in pixels). As a result,
the vectors become three-dimensional.
Typically, in self-organizing networks each neuron is connected to all compo-
nents of the input vector using synaptic connections. The weights of the neurons
synaptic connections form a vector w, which is needed to be initialized before
the training. To reduce the number of “dead” neurons, it is better to use the
uniform or random initialization. The number of neurons should be not less than
the number of lines you want to detect. Usually, it is enough to take a few dozen,
since too many neurons lead to unnecessary increase of computational costs and
increase possibility of detecting false objects.
The next step is the self-organization (learning) of the competitive type neu-
ral network (Kohonen network). There are several algorithms for learning the
self-organizing neural networks, such as the WTA algorithm (Winner Takes All),
60
Fig. 1. Hough transformation for a linear object on a binary image
the WTM algorithm (Winner Takes Most), the neural gas algorithm, and others.
These algorithms differ in the rate of convergences, in efficient use of neurons,
and in implementation complexity of calculations at each iteration. In this work,
the choice was made in favor of the neural gas algorithm with coordinate-wise
metric (“Manhattan”) [6]. The Manhattan metric has the form
N
X
d(x, wi ) = |x − wi |,
i=1
and requires a minimum computational cost among the other metrics. With
this metrics, and the neural gas one algorithm has the best convergence among
self-organization algorithms.
The weights adjustment in the neural gas algorithm is performed with the
coefficient G(i, x)
m(i)
G(i, x) = exp − ,
λ
where m(i) is the order of the neuron after sorting by the Manhattan distance.
During the neural network training, the vectors w are sequentially supplied to
the input. To improve the convergence, they are introduced to the network in a
random order. Further, taking into account the chosen metric, the neurons sort-
ing and neuron weight adjustment procedures are performed. Next, the process
repeats until predetermined number of cycles is completed (Fig. 4). After that,
61
Fig. 2. Transformation of Hough plane into training vectors array: (a) Hough plane;
(b) training vectors array
the weights fine adjustment procedure with an exponential calibration coefficient
[6, 7] is recommended to be used
F (i, x) = exp (−ad) ,
where d is the Manhattan distance and a is an experimentally adjusted coefficient
(it may decrease during network training).
As a result of the network self-organization and the weights fine adjustment,
the most neurons group near the center of training vectors clusters, but some of
them become “dead” or “wandering” (i.e., they do not find any vectors cluster).
Formed neurons groups are combined, and the “dead” and “wandering” neurons
are discarded that leads to reduction of a number of detected lines.
The peculiarity of this problem is that the neural network works only in
training mode. After the end of the training process, the weights of neurons are
determined; then they are denormalized and descaled that result coordinates of
desired straight lines. The inverse Hough transformation allows one to display
these linear structures in the spatial coordinates.
The final processing stage is the weight statistical analysis of the pixels on
the selected lines [5, 6]. It is used, at first, to find the borders of a linear structure
segment if it passes not through the full image, and, secondly, to eliminate the
62
Fig. 3. Hough plane
remaining false lines. The first problem may be solved by the correlation analysis
algorithms, and the second one is performed by the image brightness distribution
analysis [5].
3 Experimental results
The developed algorithm has been tested both on models and on real radar
images. Figure 5a shows an image obtained by the synthesized aperture radar
RADARSAT-1 (spatial resolution is 8 m). In the picture, there are images of
the four forest belts of different lengths, as well as, a bright fragment in the
lower right corner. In addition, the fragment has a horizontal stripe of a medium
brightness that is the image defect.
The resulting linear structures are presented on Fig. 5d. It is seen that all
lines are detected correctly. A line truncation in the lower right corner takes
place due to the image brightness decrease there.
Figure 6 demonstrates the RADARSAT-1 imagery processing results.
63
Fig. 4. Network training: (a) the beginning of the process (random weights initializa-
tion); (b) near the end of the process
Fig. 5. RADARSAT-1 imagery processing results: (a) original scene; (b) Hough plane;
(c) training vectors and neurons; (d) linear elements detecting process
64
Fig. 6. LANDSAT-7 imagery processing results: (a) original scene; (b) linear elements
detecting process
4 Conclusion
The proposed algorithm can significantly improve linear elements detection effi-
ciency on radar images compared with traditional algorithms. It becomes possi-
ble due to the application of a neural network, which has enough low sensitivity
to purity of the input data. Application of the neural network algorithm auto-
mates the process of linear structures detection due to the exclusion of thresholds
selection from the algorithm.
5 Acknowledgment
This work was supported by the RFBR grants nos. 13-07-12168, 13-07-00785 and
by the Ural Federal University’s Center of Excellence in “Quantum and Video
Information Technologies: from Computer Vision to Video Analytics” (according
to the Act 211 Government of the Russian Federation, contract 02.A03.21.0006)
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