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Detecting linear structures within the ASTER satellite image by effective denoising and contrast enhancement in the device independent color space.


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 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 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.


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

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] 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 {[[I.sub.min], [m.sub.0]], [[m.sub.0] + 1, [m.sub.1]],.........[[m.sub.n] + 1,[i.sub.max]] parameters used to dynamically equalize the histogram by [span.sub.i] = [high.sub.i] - [low.sub.i]

Highest and lowest intensity values contained in the partitioned histogram is factor = [span.sub.i] x [log.sub.10][M.sub.i]

[M.sub.i] is the total number of pixels present in the partitioned histogram


[[start.sub.1], [stop.sub.1]] = [0, [range.sub.1]]

[[start.sub.n+1], [stop.sub.n+1]] = [[[summation].sup.n+1.sub.k=1] [range.sub.k], 1-1]

Global Histogram Equalization method is used to equalize the partitioned histograms. The remapped values are obtained for the ith partitioned histogram is as

y(j) = [start.sub.i] [range.sub.i] [[summation].sup.i.sub.k] = [start.sub.i] h (k)/[M.sub.i]

where y(j) is the new intensity level, h(k) is the value of the histogram, [[summation].sup.i.sub.k] = [start.sub.i] h (k) 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 [M.sub.0] (x,y)

2. Increment the value of I, the data M;(x,y) is convolved with rows and then by columns along with the kernel h. and the result is [M.sub.i+1](x,y). The kernel h is [1/16, 1/4, 3/8, 1,4, 1,16] and is modified in terms of scale i by inserting ([2.sup.i-1]-1) zeros between two taps.

3. Calculate DWT [[omega].sub.i] (k) = [M.sub.i-1](k) - [M.sub.i](k)

4. Return to step 2 till scale i equals to the number k which is the deepest resolution level.

Experiments and 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.


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.






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.


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[2.] Yeong-Taeg Kim, 1997. "Contrast enhancement using brightness preserving bi-histogram equalization", IEEE Trans. Consumer Electronics, 43(1): 1-8.

[3.] Soong-Der Chen and Abd. Rahman Ramli, 2003. "Minimum mean brightness error bi-histogram equalization in contrast enhancement", IEEE Trans. Consumer Electron., 49(4): 1310-1319.

[4.] Abdullah-al-wadud, M., M.H. Kabir, M.A.A. Dewan, Oksam, 2007. Chae:, "A dynamic histogram equalization for image contrast enhancement", IEEE Trans. Consumer Electron., 53: 593-600.

[5.] Haidi Ibrahim, N.S. Pik Kong, 2007. "Brightness preserving dynamic histogram equalization for image enhancement", IEEE Trans. Consumer Electron, 53(4).

[6.] Poddar, S. et al., 2013. "Non-parametric modified histogram equalization for contrast enhancement", The Institution of Engineering and Technology, 7(7): 641-652.

[7.] MPS Kuber et al., 2015. "Improving brightness using dynamic fuzzy histogram equalization", Intl. Journal of signal processing, image processing and pattern recognition, 8(2): 303-312.

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[9.] https: //

[10.] Koteswararao and Dr. Prasad, 2014. "Decimated and Undecimated Wavelet Transform based estimation of Images", Intl. Journal of Innovative Research and Sci. Engg & Technology, 3(10): 16981-16988.

[11.] Starck, J.L. et al., 2006. "The Undecimated Wavelet Decomposition and its reconstruction", DRAFT.

[12.] Kui Jiang et al., 2015. "Isotropic undecimated wavelet transform fuzzy algorithm for retinal blood vessel segmentation", Journal of Medical Imaging and Health Informatics, 5(7).

[13.] De-Shuang Huang et al., 2015. "Intelligent Computing Theories and Methodologies", Springer.

(1) Sukumar M and (2) Nelson Kennedy Babu C

Department of Computer Science & Engineering St.Peter's Institute of Higher Education & Research, Avadi, Chennai, Tamilnadu, India Department of Computer Science & Engineering Dhanalakshmi Srinivasan College of Engineering Coimbatore, Tamilnadu, India

Received 27 May 2016; Accepted 28 June 2016; Available 12 July 2016

Address For Correspondence:

Sukumar M, Department of Computer Science & Engineering St.Peter's Institute of Higher Education & Research, Avadi, Chennai, Tamilnadu, India.

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Author:M., Sukumar; C., Nelson Kennedy Babu
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Date:Jun 30, 2016
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