4 iaetsd detecting linear structures within the aster satellite image by effective denoising

7. CONCLUSION:
Patterns generated by conventional Web Usage
Mining methods do not explicitly represent the user’s
underlying interest. Hence there is a need to include the
semantic information in web usage model to understand
web user’s navigational behaviour at conceptual Level.
So, this paper has presented a two knowledge
representation models to semantically enrich the web
usage model. One is a TermNetWP, which is
automatically constructed to represent the domain
knowledge of a website and other is a Conceptual
Prediction model. CPM integrate the web usage
knowledge with the domain knowledge (TermNetWP)
resulting in TermNavNet, a weighted semantic network
of frequently viewed terms. By semantically enhancing
the web usage knowledge, “new-item” problem is
eliminated. Two Web-page recommendation strategies
have been proposed to predict next Web-page requests of
users through querying the knowledge bases.
For the future work, we need to take into account
the up-to-date online user’s intuition as the users interest
may vary when surfing on the web for information.
REFERENCES:
[1] B. Liu, B. Mobasher, and O. Nasraoui, “Web usage
mining,” in Web Data Mining: Exploring Hyperlinks,
Contents, and Usage Data, B.Liu, Ed. Berlin, Germany:
Springer-Verlag, 2011, pp. 527–603.
[2] B. Mobasher, “Data mining for web personalization,” in
The Adaptive Web, vol. 4321, P. Brusilovsky, A. Kobsa, and
W. Nejdl, Eds. Berlin, Germany: Springer-Verlag, 2007, pp.
90–135.
[3] G. Stumme, A. Hotho, and B. Berendt, “Usage mining for
and on the Semantic Web,” in Data Mining: Next Generation
Challenges and Future Directions. Menlo Park, CA, USA:
AAAI/MIT Press, 2004.
[4] H. Dai and Mobasher, “Integrating Semantic knowledge
with web usage mining for personalization, “in Web Mining:
Applications and Techniques, A.Scime, Ed.Hershey, PA, USA:
IGIGlobal, 2005, pp.205-232.
[5] S.A. Rios and J. D. Velasquez, “Semantic Web Usage
mining by a concept-based approach for off-line website
enhancements,” in proc. WI-IAT’08, Sydney, NSW, Australia,
pp.234-241.
[6] S. Salin and P. Senkul, ”Using semantic information for
web usage mining based recommendation,” in proc.24th
ISCIS, Guzelyurt, Turkey, 2009.
[7] C. Ezeife and Y. Liu, “Fast incremental mining of Web
sequential patterns with PLWAP tree,” Data Min. Knowl.
Disc, vol. 19, no. 3, pp. 376–416, 2009.
[8]Rajimol A., and Raju G.: “FOL-Mine – “A More Efficient
Method for Mining Web Access Pattern”, Communications
in Computer and Information Science, vol .191,no.5 pp.253-
262,2011.
[9]Gopala Krishna and Achuthan Nair: ”A Novel Weighted
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4,pp.201-209,June 2014.
[10] G. Stumme, A. Hotho, and B. Berendt, “Semantic Web
mining: State of the art and future directions,” journal of
Web Semantic, vol. 4, no. 2,pp. 124–143, Jun. 2006.
[11] L. Wei and S. Lei, “Integrated recommender systems
based on ontology and usage mining,” in Active Media
Technology, vol. 5820,J. Liu, J. Wu, Y. Yao, and T. Nishida,
Eds. Berlin, Germany: Springer-Verlag, 2009, pp. 114–125.
[12] A. Loizou and S. Dasmahapatra, “Recommender
systems for the semantic Web,” in Proc. ECAI, Italy, 2006.
[13]M.O’Mahony,N.Hurley, N. Kushmerick, and G. Silvestre,
“Collaborative recommendation: A robustness analysis,”
ACM Trans. Internet Technol., vol. 4, no. 4, pp. 344–377, Nov.
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[14]Sures Shirgave and Prakash Kulkarni: “Semantically
Enriched Web Usage Mining For Predicting User Future
Movements”, IJWesT, Vol.4, No.4, October 2013.DOI:
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[15]C.Ramesh, Dr. K. V. Chalapati Rao, Dr. A. Goverdhan: “A
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Model”, IJCSIT Vol 3, No 5, Oct 2011.DOI:
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[16] B. Zhou, S. C. Hui, and A. C. M. Fong, “Efficient sequential
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[17]Cooley, B.Mobasher and J.Srivastava ,”Data Preparation
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[19]Bamshad Mobasher, Robert Cooley, and Jaideep
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142–151, 2000.
[20] F. Masseglia, P. Poncelet, and R. Cicchetti, "WebTool: An
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ISBN: 978-81-930654-7-5
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Proceedings of ICEEM-2016
©IIRDEM 201622

Detecting linear structures within the ASTER satellite
image by effective denoising and contrast
enhancement in the device independent color space
Sukumar M
Department of Computer Science & Engineering
St.Peter’s Institute of Higher Education & Research,
Avadi, Chennai, Tamilnadu, India
msukumar.btech@gmail.com
Abstract— Segmentation of linear structures (lineaments) is of
significance in the field of remote sensing but has some technical
difficulties since the size of the image is too large to process and
the color space used to acquire the image. While preprocessing this
kind of high resolution multispectral satellite images, the device
independent color space L*a*b* is preferred now-a-days and also
the preprocessing techniques is expected to preserve brightness /
contrast of the image. In this paper, ASTER image dataset is used.
Non-parametric Modified Histogram Equalization for contrast
enhancement (NMHE) and Brightness Preserving Dynamic Fuzzy
Histogram Equalization (BPDFHE) method is selected and
applied to the input image for preprocessing. Isotropic
Undecimated Wavelet transform is used to segment the texture
(lineaments) features from the preprocessed image. For effective
denoising and contrast enhancement, the combination of above
said Brightness preserving contrast enhancement method and the
undecimated wavelet transform gives better results for the ASTER
dataset images.
Keywords—Histogram Equalization (HE), IUWT, Contrast
enhancement, ASTER (key words)
I. INTRODUCTION
Multi-disciplinary approach to mineral exploration
comprise large scale and detailed mapping aided by
interpretative analysis of remotely sensed and aero geophysical
data, ground geophysical survey, geochemical prospecting and
subsurface exploration through pitting, trenching and followed
by drilling.
Geologic lineament mapping is considered as a very
important issue for problem solving in engineering in site
selection for mineral exploration, hydro geological research.
Major linear features may be used to find mineral deposits.
Linear features are topographic features such as ridges and
canyons that follow a straight line and are probably the surface
expression of a fault. Satellite imagery and high altitude aerial
photography are useful for this purpose. Mineral deposits tend
to be aligned along linear features. The intersection of linear
features is an excellent place to prospect. Lineaments may
represent deep fractures which could provide access to ore
fluids. Major goal of this research is to extract the linear
Nelson Kennedy Babu C
Department of Computer Science & Engineering
Dhanalakshmi Srinivasan College of Engineering
Coimbatore, Tamilnadu, India
cnkbabu63@gmail.com
structures / textures and extract the stock works (a zone of
intersecting faults) from the multispectral ASTER image.
Recently more and more researchers have proposed
different approaches to detect or segment linear features from
the satellite image.
Image enhancement is the basic step in most of the image
processing applications. One of the effective ways to enhance
the image is by equalizing the histogram values of the image.
Initially, the histogram equalization methods enhance the image
fully i.e. it doesn’t consider the contrast and brightness
(intensity) values present in the image. It creates undesirable
effect while post processing the image [1]. To overcome these
kinds of problems, many researchers proposed various
algorithms like Bi-Histogram Equalization (BBHE) [2]. In this
method, the image is enhanced by finding the mean value of the
histogram as a part of histogram partitioning. Minimum Mean
Brightness Error Bi-Histogram Equalization (MMBEBHE)
which is same as BBHE, it splits the histogram based on the
intensity of the image and the least mean difference is used to
equalize the image [3]. Dynamic Histogram Equalization
(DHE) first smooth the image using 1D smoothing filters and
splits the histogram based on the local minimum [4]. Brightness
Preserving Dynamic Histogram Equalization (BPDHE) is an
extension to HE which produces the output image with the same
mean intensity level of the input image which refers that the
mean brightness of the image is maintained [5]. Non parametric
Modified Histogram Equalization (NMHE) can be applied in
both grey level and color images and videos too. This method
preserves the overall content of the image and also enhances the
contrast [6]. Brightness Preserving Dynamic Fuzzy Histogram
Equalization (BPDFHE) manipulates the image histogram by
redistributing the grey level values present in the valley portion
between two consecutive peaks [7]. Brightness preserving
Fuzzy Dynamic Histogram Equalization (BPFDHE) can solve
the problems like contouring effect and the information loss in
the potential information region. This in turn improves the
crispness of the interval and the number of pixels in the interval
[8]. In the study of mechanical properties of materials,
"isotropic" means having identical values of a property in all
directions. This definition is also
ISBN: 978-81-930654-7-5
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©IIRDEM 201623

used in geology and mineralogy [9]. Stationary Wavelet
Transform otherwise called as undecimated wavelet transforms.
This is one of the powerful approach to denoise the image and
also in the field of pattern recognition. The Isotropic
Undecimated Wavelet Transform, IUWT, algorithm is well
suited for the astronomical data where the subjects of matter are
more or less isotropic in most cases [10] and [11]. Isotropic
Undecimated Wavelet Transform (IUWT) is a simple method
for denoising and segmentation [12].
The rest of the paper is organized as follows. The Second
section explains about the materials and methods. Third section
explains the experiments & results and the final section states
the conclusion and future work.
II. MATERIALS & METHODS
A. ASTER Image Dataset
In order to segment the linear features from the High
resolution Multispectral image (e.g.) ASTER satellite image is
used. ASTER is an Advanced Spaceborne Thermal Emission
and Reflection Radiometer; a multispectral imager which
covers a wide spectral region of the electromagnetic spectrum
from the Visible Near Infra Red (VNIR) to the Thermal Infra
Red (TIR). ASTER Image dataset is the best tool for the
minerals exploration application because the image acquisition
cost is low. ASTER image covers large area. The availability of
ASTER data is also easy. It can accurately map lithologic and
mineralogical units on the surface. VNIR data at 15m resolution
is currently the best resolution multispectral satellite data
available commercially.
B. Non parametric Modified Histogram Equalization
Non-parametric Modified Histogram Equalization (NMHE)
[6] holds an independent parameter setting for dynamic range
of images. In addition, it removes spikes and also it doesn’t
need any additional parameters to be given manually to every
image. This method is able to process only the gray scale
images. The procedure for NMHE is given as follows:
1. Remove spikes from the histogram
a) Compute the modified histogram by comparing the
dissimilar pixels with its neighbors
b) Normalize the modified histogram
c) Calculate the measure of un-equalization (Mu)
2. Clip the histogram and find the measure of un-equalization
(Mu)
3. Obtain modified probability density function based on the
“Mu” factor
4. Obtain modified histogram equalized image
C. Brighness Preserving Dynamic Fuzzy Histogram
Equalization
Brightness preserving dynamic fuzzy histogram
equalization [BPDFHE] technique equalize the image
histogram by distributing the gray values present in the valley
portions of the histogram. It clearly shows that no remapping
of the histogram peaks takes place. This method is used in both
grayscale and color images. The BPDFHE technique consists
of following operational stages:
1. Change the input image to the L*a*b color space
2. Computation of fuzzy histogram
a. Produce the smooth histogram
h(i) is the frequency rate of gray levels
µi(x,y)i is the triangular fuzzy membership function
i(x,y) is the grey values as a fuzzy number
[a,b] is the triangular membership function
3. Partition the histogram based on the “local maxima” value.
where h’(i) is the first order derivative of fuzzy histogram
h(i) corresponds to the ith
intensity level.
To reduce the approximation errors, second order derivative
is calculated from the fuzzy histogram
4. Equalize every partitioned histograms dynamically
Partitioning the histograms based on
{[Imin,m0],[m0+1,m1],………[mn+1,imax] parameters used to
dynamically equalize the histogram by
spani=highi-lowi
Highest and lowest intensity values contained in the
partitioned histogram is
factor=spani x log10Mi
Mi is the total number of pixels present in the partitioned
histogram
[start1, stop1]=[0, range1]
[startn+1, stopn+1]=[ , l-1]
Global Histogram Equalization method is used to equalize
the partitioned histograms. The remapped values are
obtained for the ith
partitioned histogram is as
ISBN: 978-81-930654-7-5
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©IIRDEM 201624

where y(j) is the new intensity level, h(k) is the value of the
histogram, is the total population count in
the partitioned fuzzy histogram.
5. Normalizing the brightness of the image
D. Isotropic Undecimated Wavelet Transform
Isotropic undecimated wavelet transform is suitable for
astronomical imaging. It decomposes the image into different
scales. IUWT introduces a multi resolution algorithm for
detecting bright spots. The feature detection is the process of
extracting and combining multilevel elements of response, with
each element coming from successive resolution level. To keep
the significant response of the filter to the desired feature, the
denoising technique uses hard thresholding value. Finally, the
newly selected coefficient allows us to combine multi scale
information to detect the spots. But, its performance is slightly
poor in case of low quality images, at that time, soft
thresholding is used; instead of hard thresholding [13].
1. Initialize i to 0, starting with the original image M0(x,y)
2. Increment the value of I, the data Mi(x,y) is convolved with
rows and then by columns along with the kernel h. and the
result is Mi+1(x,y). The kernel h is [ ] and is
modified in terms of scale i by inserting (2i-1
-1) zeros
between two taps.
3. Calculate DWT
4. Return to step 2 till scale i equals to the number k which is
the deepest resolution level.
III. EXPERIMENTS & RESULTS
In this work, ASTER satellite image is used as input to the
system and apply the above said algorithms and measure the
Absolute Mean Brightness Error (AMBE) and PSNR values.
AMBE is the absolute difference between the mean of input and
output images.
Fig.3.1. Loading ASTER Satellite Image
Lower the AMBE depicts the better brightness preservation
in the image and Higher the PSNR gives the good
contrast enhancement. From the experiments and the values of
AMBE and PSNR, BPDFHE technique is better when
compared with the NMHE method. And the resultant
segmentation from the preprocessed enhancement images is
quite satisfactory in lineament detection.
Fig.3.2. Change in Color space (RGB to CIELAB)
Fig.3.3. Enhanced Image using NMHE
Fig.3.4. Image enhancement using BPDFHE
ISBN: 978-81-930654-7-5
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©IIRDEM 201625

Fig.3.5. Edge detection using IUWT
Fig.3.6. Mapping of lineaments in the input image
IV. CONCLUSION & FUTURE WORK
In this paper, linear structures are detected within the
ASTER satellite image by using the effective denoising and
contrast enhancement methods. Isotropic Undecimated
Wavelet Transform is mainly used in the field of medical image
processing to segment the vessels. IUWT along with the
BPDFHE technique enhances the bright spots present in the
satellite image. In Minerals targeting system, geologic
lineaments need to be extracted. But the complexity in detecting
those lineaments is: One side of the lineament looks brighter
and the other side is not. In this work, the image is effectively
denoised and contrast is enhanced and some of the linear
structures are detected. In the future work, sensitive shape
optimization algorithms planned to be adopted for better
lineament detection.
REFERENCES
[1] Chen, S.-D., Rahman Ramli,A.: “Preserving brightness in histogram
equalization based contrast enhancement techniques”, Digital Signal
Process., 2004, 14, pp.413-428
[2] Yeong-Taeg Kim, “Contrast enhancement using brightness preserving bi-
histogram equalization”, IEEE Trans. Consumer Electronics, vol.43, no.1,
pp. 1-8, Feb. 1997
[3] Soong-Der Chen and Abd. Rahman Ramli, “Minimum mean brightness
error bi-histogram equalization in contrast enhancement”, IEEE Trans.
Consumer Electron., vol.49, no.4, pp.1310-1319, Nov. 2003.
[4] Abdullah-al-wadud, M.,Kabir, M.H.,Dewan, M.A.A., Oksam, Chae:, “A
dynamic histogram equalization for image contrast enhancement”, IEEE
Trans. Consumer Electron., 2007, 53, pp. 593 - 600
[5] Haidi Ibrahim,N.S.Pik Kong, “Brightness preserving dynamic histogram
equalization for image enhancement”, IEEE Trans. Consumer Electron,
Vol.53, No.4, Nov 2007
[6] S.Poddar et al., “Non-parametric modified histogram equalization for
contrast enhancement”, The Institution of Engineering and Technology,
Vol.7, Iss.7, pp. 641-652, 2013.
[7] MPS Kuber et al., “Improving brightness using dynamic fuzzy histogram
equalization”, Intl. Journal of signal processing, image processing and
pattern recognition, Vol.8, No.2, pp.303-312, 2015
[8] Abd. Sarrafzadeh et al., “Brightness preserving fuzzy dynamic histogram
equalization”, Proceedings of the Intl. multi conference of engineers and
computer scientists, vol.1, Mar. 2013.
[9] https://en.wikipedia.org/wiki/Isotropy
[10] Koteswararao and Dr.Prasad, “Decimated and Undecimated Wavelet
Transform based estimation of Images”, Intl. Journal of Innovative
Research & Sci. Engg & Technology, Vol.3, Issue:10, pp. 16981-16988,
2014
[11] J.L.Starck et al., “The Undecimated Wavelet Decomposition and its
reconstruction”, DRAFT, 2006
[12] [12] Kui Jiang et al., “Isotropic undecimated wavelet transform fuzzy
algorithm for retinal blood vessel segmentation”, Journal of Medical
Imaging and Health Informatics, vol.5, No.7, Nov. 2015.
[13] De-Shuang Huang et al., “Intelligent Computing Theories and
Methodologies”, Springer, Aug. 2015
M.Sukumar received his B.Tech. degree in
Information Technology from Anna University,
Chennai, India in the year 2007 and M.Tech degree
in Computer and Information Technology from the
Center for Information Technology and Engineering
of Manonmaniam Sundaranar University,
Tirunelveli, India in the year 2011. Currently, he is an
Assistant Professor in the Department of Information
Technology, Sri Vidya College of Engineering &
Technology, Virudhunagar, India and also pursuing
Ph.D in St.Peters University,
Chennai, India. His research interests include Image Processing, Remote
Sensing and He is the Student member of UACEE.
C.Nelson Kennedy Babu received his M.Sc Degree
from Madurai Kamaraj University, Madurai, India
and M.Tech degree in Computer and Information
Technology from Center for Information
Technology and Engineering of Manonmaniam
Sundaranar University, Tirunelveli, India in the
year 2004 and Ph.D degree in Computer Science
from Madurai Kamaraj University, Madurai, India
in the year 2009. He has more than two decades of
service in the field of computer science and
engineering.
Currently, he is the Professor in the department of Computer Science and
Engineering of Dhanalakshmi Srinivasan College of Engineering, Coimbatore,
India. His research interests include Signal and Image Processing, Remote
Sensing, Visual Perception, Mathematical Morphology and Pattern
Recognition. He is the senior member of IEEE.
ISBN: 978-81-930654-7-5
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©IIRDEM 201626

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