05-07-2013, 03:13 PM
Satellite Image Processing with MATLAB
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Introduction
MATLAB (MATrix LABoratory) integrates computation, visualization, and programming in
an easy-to-use environment where problems and solutions are expressed in familiar
mathematical notation. MATLAB features a family of application-specific solutions called
toolboxes. Toolboxes are comprehensive collections of MATLAB functions (M-files) that
extend the MATLAB environment to solve particular classes of problems. Areas in which
toolboxes are available include signal processing, control systems, neural networks, fuzzy
logic, wavelets, simulation, image processing and many others. Image processing tool box
has extensive functions for many operations for image restoration, enhancement and
information extraction. Some of the basic features of the image processing tool box are
explained and demonstrated with the help of a satellite imagery obtained from IRS (Indian
Remote Sensing Satellite) LISS III data of Uttara Kannada district, Karnataka.
Images in MATLAB and the Image Processing Toolbox
The basic data structure in MATLAB is the array of an ordered set of real or complex
elements. This object is naturally suited to the representation of images, real-valued, ordered
sets of color or intensity data. MATLAB stores most images as two-dimensional arrays, in
which each element of the matrix corresponds to a single pixel in the displayed image.
For example, an image composed of 200 rows and 300 columns of different colored
dots would be stored in MATLAB as a 200-by-300 matrix. Some images, such as RGB,
require a three-dimensional array, where the first plane in the third dimension represents the
red pixel intensities, the second plane represents the red and green pixel intensities, and the
third plane represents the blue pixel intensities.
This convention makes working with images in MATLAB similar to working with
any other type of matrix data, and renders the full power of MATLAB available for image
processing applications. For example, a single pixel can be selected from an image matrix
using normal matrix subscripting.
Image Arithmetic
Image arithmetic is the implementation
of standard arithmetic operations, such
as addition, subtraction, multiplication,
and division, on images. Image
arithmetic has many uses in image
processing both as a preliminary step
and in more complex operations. For
example, image subtraction can be used
to detect differences between two or
more images of the same scene or
object.
Analyzing and Enhancing Images
The Image Processing Toolbox supports a range of standard image processing operations for
analyzing and enhancing images. Its functions simplify several categories of tasks, including:
• Obtaining pixel values and statistics, which are numerical summaries of data in an
image.
• Analyzing images to extract information about their essential structure.
• Enhancing images to make certain features easier to see or to reduce noise.
Pixel Selection
The toolbox includes two functions that provide information about the color data values of
image pixels specified. The pixval function interactively displays the data values for pixels as
the cursor is moved over the image. pixval can also display the Euclidean distance between
two pixels. The impixel function returns the data values for a selected pixel or set of pixels.
You can supply the coordinates of the pixels as input arguments, or you can select pixels
using a mouse.