International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 1- January 2016
Classification of Chennai scene using Remote Sensing data and Image
processing techniques
Sheena A D*, Dr. C. Udhayakumar**
*
Research Scholar,Department of Civil Engineering,
Anna University, Chennai, India.
**
Associate Professor, Department of Civil Engineering,
Anna University, Chennai, India.
Abstract – The earth features are captured and recorded each
day. To study the land cover changes occurred for the entire
earth it’s a matter of few hours. Our Satellite technology
captures as images with detailed information. Working on to
theimages using the latest image processing techniques, the
classifications are obtained.
Keywords –Image Processing, Image Enhancement, Filtering
Techniques, Landsat
I.
INTRODUCTION
1. IMAGERY DATA
The satellite image data when manipulatedwith values and
positions, it is possible to see features that would not normally
be visible to our eyes. The level of brightness or reflectance of
light from the surfaces in the image is helpful with vegetation
crop studies, mineral exploration, water bodies, feature
extraction etc.
2. DATA COLLECTION
2.1 LANDSAT TM 7 BANDS
Remote Sensing is the science of acquiring, processing and
interpreting images that record the interaction between
electromagnetic energy and matter(F.F. Sabins).In an image,
there are various spectral band data combinedto get
information. By using different bands that represent spectrum
which is not visible to our human eye, i.e., thermal, infrared,
etc
are
also
seen.
Fig. 1 – All 7 bands of LANSAT image
The satellite is equipped with an Enhanced Thematic Mapper
Plus (ETM+) that is an eight-band multispectral scanning
radiometer capable of providing data of the Earth's surface.
A toposheetis a map characterized by large-scale detailed
topographic information and quantitative representation of
reliefusing contour lines.
3. IMAGE PROCESSING
Image processing is used to enhance data of visual
interpretation. Digital processing and analysis are carried out
to automatically identify targets and extract information
completely without manual intervention. Digital analysis is
useful for simultaneous analysis of many spectral bands and
can process large data sets much faster than a human
interpreter. In manual interpretation the results will vary with
different interpreters where as in digital analysis it is based on
the manipulation of digital numbers in a computer coding
resulting in more consistent good accurate results.
3.1DIGITAL PROCESSING
An image may be defined as a two-dimensional function,
f(x,y), where x and y are spatial coordinates, and the
amplitude of f at any pair of coordinates (x,y) is called the
intensity or grey level of the image at that point (Rafael C.
Gonzalez, Richard E.Woods, Steven L.Eddins).Digital image
processing involvesvarious procedures with formatting and
correcting the data, enhancing the digital images to facilitate
better visual interpretation. To process remote sensing images
digitally, the digital format of DN values are interpolated.
Visual Interpretation is identified based on color, tone, shape,
size, texture,pattern, shadow, topography and association.
Preprocessingare radiometric corrections include correcting
the data for sensor irregularities and converting the data so
they accurately represent the reflected or emitted radiation
measured by the sensor. The geometric registration
processinvolves identifying the image coordinates of clearly
distinct points on the ground called ground control points.
This is image-to-map registration. Image to image registration
is the translation and rotational alignment process by which
two images of like geometry and of the same geographic area
are positioned coincident with respect to one another so that
corresponding elements of the same ground area appear in the
same place on the registered images (Chen and Lee, 1992).
The resampling process calculates the new pixel values from
the original digital pixel values in the uncorrected image.
2.2 TOPOSEET/ IMAGE
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 1- January 2016
3.4NDVI
The Normalized Difference Vegetation Index (NDVI) is an
index of plant “greenness” or photosynthetic activity, and is
one of the most commonly used vegetation indices. NDVI is
calculated on a per-pixel basis as the normalized difference
between the red and near infrared bands from an image.
Vegetation indices are based on the observation that different
surfaces reflect different types of light differently.
NDVI = (NIR – Red) / (NIR + Red)…………….(2)
Fig.2Resampling method
EXTRACTING AREA OF INTEREST
II. METHODOLOGY
These are the following step by step basic procedure for
working with satellite images.
Image Processing
Technique
Selecting Area of Interest or Image Subset is one of the
processes of Image preprocessing which helps to reduce the
image of our AOI spatially. The AOI is chosenin order to
process the required area based on the requirement.
Reading Satellite
Data
STUDY AREA
Here, the Chennai Metropolitan Administration (CMA)
boundary is taken as the Area of Interest (AOI) as study area.
Georeferencing a
Base
3.2. IMAGE ENHANCEMENT
Image enhancement is to improve the appearance of the images
to assist in visual interpretation and analysis. Examples of
enhancement functions include contrast stretching to increase
the tonal distinction between various features in a scene, and
spatial filtering to enhance or suppress specific spatial patterns
in an image.
Geometric
Correction to
Satellite Image
Band
Ratioing,
NDVI
Extracting AOI
Filtering
Image Enhancement
Methods
Image Transformation
Analysis
PCA
Fig.3 Histogram Equilization
Enhanced Images
3.3 BAND RATIOING
Band ratioingis dividing the pixels in one band with the
corresponding pixels in the second band.It has the effect of
removing the shadows.
For each pixel, divide the DN value of one band by the value of
another band.
BR = near-infrared/ red (NIR/R) ……………..(1)
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Image Classification
Analysis
Unsupervised
Supervised
Fig.4 Methodology of Image Processing Techniques for
Chennai CMA Boundary
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 1- January 2016
III.
RESULTS
4.1. FILTERING TECHNIQUES
SPATIAL FILTERING
Filtering encompasses set of digital processing functions used
to enhance the appearance of an image with reference to kernel
windows. Filters are designed to highlight or suppress specific
features in an image based on their spatial frequency.
3x3 Horizon 3x3 vertical 3x3 Summary
3x3 Horizontal 3x3 Cross Edge 3x3 Horizontal
Reduction
Detection
Edge Detection
a)Smoothening
b) Sharpening
STATISTICAL FILTERING
Maximum filters
Majority filters
Mean filters
3x3 Laplacian
3x3 Edge
Edge Detection Detection
3x3 Left
Diagonal
Minimum filters
3x3 Edge Detection
Fig.5. Filtering Methods
4.2. IMAGE TRANSFORMATIONS
Image transformations are image enhancement done to
enhance the images to give more visual information like
enhanced features.
CONVOLUTION FILTERING
3x3 Edge
Enhancement
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3x3 Low pass 3x3 High pass
filter
filter
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 1- January 2016
PRINCIPAL COMPONENT ANALYSIS
UNSUPERVISED CLASSIFICATION
Thetransformation is used to reduce the number of bands in the
data, and compress as much of the information in the original
bands into less bands.
Spectral classes are grouped first, based on the numerical
information in the data, andare then matched by the interpreter
to information classes programs, calledclustering algorithms.
The computer is required to group pixels with similar spectral
characteristics into unique clusters according to some
statistically determined criteria (Jahne, 1991).
SUPERVISED CLASSIFICATION
In a supervised classification, the interpreter identifies in the
image homogeneous representative samples based on color of
the different surface cover types of information classes. These
samples are referred to as training areas thus giving results of
land use/ land cover changes of that area.
Fig.7. Unsupervised Classification
Fig. 6. Results of PCA Transformation
4.3. IMAGE CLASSIFICATION AND ANALYSIS
It is used to digitally identify and classify pixels in the data
using Image classification and analysis operations.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 1- January 2016
Fig.8. Supervised Classification
IV.
CONCLUSION
Image processing techniques helps to enhance the images and
interpret the land cover and land use changes. The scope of the
future work is to work on enhancement algorithms and
accuracy level is tested.This is useful for various applications
such as groundwater exploration, land use planning, land cover
changes, disaster management, desertification, geo-engineering
and so on.
REFERENCES
”Wavelets For Medical Image Fusion”-Megan Howell Jones
Fundamentals Of Remote Sensing-A Canada Centre For Remote Sensing
Remote Sensing Tutorial
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4. Essential Image Processing And Gis For Remote Sensing- JianGuo Liu,
Philippa J. Mason , Uk
5. Remote Sensing Digital Image Processing- An Introduction - John A.
Richards · XiupingJia
6. Digital Image Processing - William K. Pratt, California
7. Digital Image Processing - Rafael C. Gonzalez, Richard E. Woods
8. Introductory Digital Image Processing 3rd Edition - John R. Jensen
9. Remote Sensing : Models and Methods for Image Processing, by Robert
A. Schowengerdt, 2nd edition (July 1997), Academic Pr
10. Remote Sensing : Principles and Interpretation, by Floyd F. Sabins, 3rd
edition (August 1996), W H Freeman & Co
11. Remote Sensing and Image Interpretation, by Thomas M. Lillesand,
Ralph W. Kiefer, 4th edition (October 1999) , John Wiley & Sons
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2.
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