This document discusses techniques for enhancing satellite images using linear contrast stretching and intensity level slicing. It begins with an abstract describing image enhancement and its purpose of improving visual appearance and aspects of images. It then provides background on sources of image degradation and the need for enhancement. The document describes two techniques - linear contrast stretching and intensity level slicing. It provides examples of their application and results, showing how they can increase contrast and emphasize certain intensity levels. In conclusion, it states that choice of enhancement technique depends on image characteristics and application, and computational cost is important for real-time use.

1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 6, Issue 2, February (2015), pp. 01-05 © IAEME
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FEATURE ISOLATION AND EXTRACTION OF
SATELLITE IMAGES FOR REMOTE SENSING
APPLICATIONS
Abhay M S Aradhya1
, Kishore Nayak M2
, Rahul A. R3
1,2,3
Sri Jayachamarajendra College of Engineering (SJCE), Mysore, Karnataka, India – 570 006
ABSTRACT
Image enhancement is to improve the visual appearance of an image, or to provide a “better
transform representation for future automated image processing. The reason behind image
enhancement methods is to raise image visibility and aspects. Image enhancement is one of the ways
as they improve the quality of the image by increasing dominance of some aspects or by decreasing
uncertainty among unusual areas of the image. Image enhancement methods exist of a collected
works of techniques that try to find to improve the visual appearance of an image or to convert
the image to a form enhanced appropriate for investigation by anyone or any piece of tools. Several
images bear from poor contrast and therefore, it is vital to enhance the contrast. Contrast
enhancement is one of the most demanding concerns in low level image processing. Contrast
enhancement methods are utilized for the betterment of the visual observation and colour
facsimile of low contrast images. This work presents a Linear Contrast stretch and intensity level
slicing technique to enhance images obtained through satellites.
Keywords: Image Enhancement, Intensity Level Slicing, Thresholding, Linear Contrast Stretch
INTRODUCTION
Digital images often suffer from poor contrast and noise from various sources, such as
different illumination conditions, image quantization, compression and transmission, etc,. These
sources of image degradation normally arise during image acquisition and processing and have direct
bearing on the quality of the image [1].
In order to interpret image correctly enhancement is necessary to reduce/remove the image
degradation [2]. Of particular to this study is the work related to image enhancement by linear
contrast stretch and intensity level slicing.
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Consider an 8-bit grey level input image f(x, y) having pixel value ranging from 0 - 255. It
can transform input image f(x, y) into g(x, y), which indicates processed output. T represents an
operation on image ’f’ defined over some neighborhood of (x, y).
It can be expressed as
G (x, y) =T [f(x, y)]…. (1)
Grey level transformation function can be written as
s = T(r)……………… (2)
Where r and s are intensity of pixels of f(x, y) and g(x, y) [3]
Basically, all satellite image-processing operations can be grouped into three categories:
Image Rectification and Restoration, Enhancement and Information Extraction. The former deals
with initial processing of raw image data to correct geometric distortion, to calibrate the data
radiometrically and to eliminate noise present in the data. The enhancement procedures are applied
to image data in order to effectively display the data for subsequent visual interpretation. It involves
techniques for increasing the visual distinction between features in a scene. The objective of the
information extraction operations is to replace visual analysis of the image data with quantitative
techniques for automating the identification of features in a scene. This involves the analysis of
multispectral image data and the application of statistically based decision rules for determining the
land cover identity of each pixel in an image. The intent of classification process is to categorize all
pixels in a digital image into one of several land cover classes or themes. This classified data may be
used to produce thematic maps of the land cover present in an image.
IMAGE ENHANCEMENT TECHNIQUES
Contrast stretching
Contrast stretching enhances image by enhancing contrast Between various parts of the
original image. The basic idea is to improve the image quality by increasing the dynamic
range of gray levels [4]. Contrast enhancement techniques expand the range of brightness values
in an image so that the image can be efficiently displayed in a manner desired by the analyst. The
density values in a scene are literally pulled farther apart, that is, expanded over a greater range. The
effect is to increase the visual contrast between two areas of different uniform densities. This enables
the analyst to discriminate easily between areas initially having a small difference in density.
(a) Original Image (b) Histogramof original Image

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(c) Moderate contrast stretched image (d) Histogram of Figure 1(c) Min. clip = 0.17,
Max. clip = 0.64
(e) Highly contrast stretched image (f) Histogram of Figure 1 (e) Min. clip = 0.24,
Max. clip = 0.35
Figure 1: Window – level contrast stretching of an earth satellite image.
Figure 1 shows the result of our simulation of a poorly digitized original satellite image along
with gray scale histograms of the original and enhanced pictures.
Gray level slicing
A grey level slicing technique enhances all the gray levels by using high values and
all other gray levels using low values (Figure 2). This function is mostly useful to enhance
flaws in X-ray images and enhancing features such as masses of water in satellite imagery. Figure
2 shows the transformation functions for the range of pixels of interest and also shows the effect on
the image. The transformation function shown in Figure 2(a) is used to enhance the beach area in the
satellite test image while preserving the background, transformation function shown in Figure 2(b) is
used to obtain the same desired area while neglecting the background.

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6464(Print), ISSN 0976 – 6472(Online), Volume 6, Issue
Figure 2: Level slicing contrast modification functions (a) Image background scal
transformation, (b) Zero background scaling transformation
(a)
Figure 3: Gray level slicing a) original image, b) With background retention, and c) Without
Gray level slicing on the original image
fuctions shown in Figure 2. Figure 3(b) and Figure 3(c) shows the result of the transformation
fuctions shown in Figure 2(a) and Figure 2(b) respectively.
The gray level slicing technique function can
diminish all others or it can emphasize a group of gray
Applications of image enhancement
1. Medical imaging where automatic contrast enhancement and
as X-ray, Digital mammography, CT scan etc.
2. Helps to improve the luminance and brightness of LCD panel which tends to brighter and
clearer onscreen images.
3. It brightens and darkens the dusk and dawn viewing.
4. Improves images for fog and sand storm environments.
CONCLUSION
Image enhancement techniques provide different approaches for improving the visual
characteristics of an image. To make choice between these techniques depends upon various
factors such as image content, viewing conditions, characteristics by observer and specific
task. Thresholding and Contrast stretching techniques are applied by stretching the grey levels
of an image. Grey level slicing technique is applied to some specifie
paper, authors have not discussed about computational cost of these algorithms while making choice
within these techniques for real time applications.
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976
6472(Online), Volume 6, Issue 2, February (2015), pp. 01-05 © IAEME
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Level slicing contrast modification functions (a) Image background scal
transformation, (b) Zero background scaling transformation
(b)
Gray level slicing a) original image, b) With background retention, and c) Without
background retention
Gray level slicing on the original image (Figure 3(a)) are carried out using the transformation
fuctions shown in Figure 2. Figure 3(b) and Figure 3(c) shows the result of the transformation
fuctions shown in Figure 2(a) and Figure 2(b) respectively.
slicing technique function can be either highlight a group of intensities and
diminish all others or it can emphasize a group of gray levels and leave the rest unaltered
enhancement
Medical imaging where automatic contrast enhancement and sharpening is needed such
ray, Digital mammography, CT scan etc.
luminance and brightness of LCD panel which tends to brighter and
It brightens and darkens the dusk and dawn viewing.
or fog and sand storm environments.
Image enhancement techniques provide different approaches for improving the visual
characteristics of an image. To make choice between these techniques depends upon various
content, viewing conditions, characteristics by observer and specific
holding and Contrast stretching techniques are applied by stretching the grey levels
of an image. Grey level slicing technique is applied to some specified region in an image. In this
paper, authors have not discussed about computational cost of these algorithms while making choice
within these techniques for real time applications.
Technology (IJECET), ISSN 0976 –
© IAEME
Level slicing contrast modification functions (a) Image background scaling
transformation, (b) Zero background scaling transformation
(c)
Gray level slicing a) original image, b) With background retention, and c) Without
(Figure 3(a)) are carried out using the transformation
fuctions shown in Figure 2. Figure 3(b) and Figure 3(c) shows the result of the transformation
be either highlight a group of intensities and
levels and leave the rest unaltered.
sharpening is needed such
luminance and brightness of LCD panel which tends to brighter and
Image enhancement techniques provide different approaches for improving the visual
characteristics of an image. To make choice between these techniques depends upon various
content, viewing conditions, characteristics by observer and specific
holding and Contrast stretching techniques are applied by stretching the grey levels
d region in an image. In this
paper, authors have not discussed about computational cost of these algorithms while making choice

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6464(Print), ISSN 0976 – 6472(Online), Volume 6, Issue 2, February (2015), pp. 01-05 © IAEME
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REFERENCES
1. N. Damera – Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image
quality assessment based on a degradation model”, IEEE Trans. Image Process., vol. 9, no. 4,
pp. 636-650, Apr. 2000.
2. D. Marr, Vision: A Computational Investigation into the Human Represent and Processing of
Visual Information. San Francisco, CA: W.H.Freeman, 1982.
3. S. Jayaraman, T. Veerakumar, and S. Esakkirajan, Digital Image Processing, Tata Mc Graw
Hill, 2009.
4. Song Tie-Rui and Zheng Wei “The Research of X-ray Bone Fracture Image
Enhancement Algorithms”, IEEE International Conference on Computer, Mechatronics,
Control and Electronic Engineering,2010.
5. Mr. Ganesh L. Attarde, Mr. G.D.Bonde and Mr. O.K.Firke, “A New Adaptive Weight
Algorithm To Remove Salt and Pepper Noise From Satellite Images” International journal of
Computer Engineering & Technology (IJCET), Volume 4, Issue 6, 2013, pp. 256 - 261, ISSN
Print: 0976 – 6367, ISSN Online: 0976 – 6375.
6. Lalit Saxena, “Effective Thresholding of Ancient Degraded Manuscript Folio Images”
International journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5,
2013, pp. 285 - 291, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
7. Manoj R. Tarambale and Nitin S. Lingayat, “The Performance of Various Thresholding
Algorithms For Segmentation Of Biomedical Image” International Journal of Advanced
Research in Engineering & Technology (IJARET), Volume 5, Issue 4, 2014, pp. 119 - 130,
ISSN Print: 0976-6480, ISSN Online: 0976-6499.