A Brief Overview of Digital Image Processing
Digital image processing deals with manipulation of digital images through a
digital computer. DIP focuses on developing a computer system that is able to
perform processing on an image. The input of that system is a digital image and the
system that processes the image using efficient algorithms to give an image as an
output.
Digital Image Processing is largely concerned with four basic operations: image
restoration, image enhancement, image classification, image transformation. Image
restoration is concerned with the correction and calibration of images in order to
achieve as faithful a representation of the earth surface as possible—a fundamental
consideration for all applications. Image enhancement is predominantly concerned
with the modification of images to optimize their appearance to the visual system.
Visual analysis is a key element, even in digital image processing, and the effects
of these techniques can be dramatic. Image classification refers to the computer-
assisted interpretation of images—an operation that is vital to GIS. Finally, image
transformation refers to the derivation of new imagery as a result of some
mathematical treatment of the raw image bands.
In order to undertake the operations listed in this section, it is necessary to have
access to Image Processing software such as ERDAS IMAGINE, ENVI, IDRISI,
ILWIS, ARC GIS etc. which are primarily GIS software systems, but also offer a
full suite of image processing capabilities.
Image Restoration
Remotely sensed images of the environment are typically taken at a great distance
from the earth's surface. As a result, there is a substantial atmospheric path that
electromagnetic energy must pass through before it reaches the sensor. Depending
upon the wavelengths involved and atmospheric conditions (such as particulate
matter, moisture content and turbulence), the incoming energy may be
substantially modified. Image restoration seeks to remove these degradation
effects. Broadly, image restoration can be broken down into the two sub-areas of
radiometric restoration and geometric restoration.
Radiometric Restoration
Radiometric restoration refers to the removal or diminishment of distortions in the
degree of electromagnetic energy registered by each detector. A variety of agents
can cause distortion in the values recorded for image cells. Some of the most
1
, common distortions for which correction procedures exist include: uniformly
elevated values, due to atmospheric haze, which preferentially scatters short
wavelength bands (particularly the blue wavelengths); striping, due to detectors
going out of calibration; random noise, due to unpredictable and unsystematic
performance of the sensor or transmission of the data; and scan line drop out, due
to signal loss from specific detectors.
Geometric Restoration
For mapping purposes, it is essential that any form of remotely sensed imagery be
accurately registered to the proposed map base. With satellite imagery, the very
high altitude of the sensing platform results in minimal image displacements due to
relief. It is therefore necessary to use photogrammetric rectification to remove
these distortions and provide accurate map measurements.
Image Enhancement
Image enhancement is concerned with the modification of images to make them
more suited to the capabilities of human vision. Regardless of the extent of digital
intervention, visual analysis invariably plays a very strong role in all aspects of
remote sensing. Some of the image enhancement techniques are Contrast Stretch,
Composite Generation and Digital Filtering Digital sensors
Image Classification
Image classification refers to the computer-assisted interpretation of remotely
sensed images. The procedures involved are treated in detail in the IDRISI Guide
to GIS and Image Processing Volume 2 chapter Classification of Remotely Sensed
Imagery. This section provides a brief overview. Although some procedures are
able to incorporate information about such image characteristics as texture and
context, the majority of image classification is based solely on the detection of the
spectral signatures (i.e., spectral response patterns) of land cover classes. The
success with which this can be done will depend on two things: 1) the presence of
distinctive signatures for the land cover classes of interest in the band set being
used; and 2) the ability to reliably distinguish these signatures from other spectral
response patterns that may be present. There are two general approaches to image
classification: supervised and unsupervised. They differ in how the classification is
performed. In the case of supervised classification, the software system delineates
specific landcover types based on statistical characterization data drawn from
known examples in the image (known as training sites). With unsupervised
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Digital image processing deals with manipulation of digital images through a
digital computer. DIP focuses on developing a computer system that is able to
perform processing on an image. The input of that system is a digital image and the
system that processes the image using efficient algorithms to give an image as an
output.
Digital Image Processing is largely concerned with four basic operations: image
restoration, image enhancement, image classification, image transformation. Image
restoration is concerned with the correction and calibration of images in order to
achieve as faithful a representation of the earth surface as possible—a fundamental
consideration for all applications. Image enhancement is predominantly concerned
with the modification of images to optimize their appearance to the visual system.
Visual analysis is a key element, even in digital image processing, and the effects
of these techniques can be dramatic. Image classification refers to the computer-
assisted interpretation of images—an operation that is vital to GIS. Finally, image
transformation refers to the derivation of new imagery as a result of some
mathematical treatment of the raw image bands.
In order to undertake the operations listed in this section, it is necessary to have
access to Image Processing software such as ERDAS IMAGINE, ENVI, IDRISI,
ILWIS, ARC GIS etc. which are primarily GIS software systems, but also offer a
full suite of image processing capabilities.
Image Restoration
Remotely sensed images of the environment are typically taken at a great distance
from the earth's surface. As a result, there is a substantial atmospheric path that
electromagnetic energy must pass through before it reaches the sensor. Depending
upon the wavelengths involved and atmospheric conditions (such as particulate
matter, moisture content and turbulence), the incoming energy may be
substantially modified. Image restoration seeks to remove these degradation
effects. Broadly, image restoration can be broken down into the two sub-areas of
radiometric restoration and geometric restoration.
Radiometric Restoration
Radiometric restoration refers to the removal or diminishment of distortions in the
degree of electromagnetic energy registered by each detector. A variety of agents
can cause distortion in the values recorded for image cells. Some of the most
1
, common distortions for which correction procedures exist include: uniformly
elevated values, due to atmospheric haze, which preferentially scatters short
wavelength bands (particularly the blue wavelengths); striping, due to detectors
going out of calibration; random noise, due to unpredictable and unsystematic
performance of the sensor or transmission of the data; and scan line drop out, due
to signal loss from specific detectors.
Geometric Restoration
For mapping purposes, it is essential that any form of remotely sensed imagery be
accurately registered to the proposed map base. With satellite imagery, the very
high altitude of the sensing platform results in minimal image displacements due to
relief. It is therefore necessary to use photogrammetric rectification to remove
these distortions and provide accurate map measurements.
Image Enhancement
Image enhancement is concerned with the modification of images to make them
more suited to the capabilities of human vision. Regardless of the extent of digital
intervention, visual analysis invariably plays a very strong role in all aspects of
remote sensing. Some of the image enhancement techniques are Contrast Stretch,
Composite Generation and Digital Filtering Digital sensors
Image Classification
Image classification refers to the computer-assisted interpretation of remotely
sensed images. The procedures involved are treated in detail in the IDRISI Guide
to GIS and Image Processing Volume 2 chapter Classification of Remotely Sensed
Imagery. This section provides a brief overview. Although some procedures are
able to incorporate information about such image characteristics as texture and
context, the majority of image classification is based solely on the detection of the
spectral signatures (i.e., spectral response patterns) of land cover classes. The
success with which this can be done will depend on two things: 1) the presence of
distinctive signatures for the land cover classes of interest in the band set being
used; and 2) the ability to reliably distinguish these signatures from other spectral
response patterns that may be present. There are two general approaches to image
classification: supervised and unsupervised. They differ in how the classification is
performed. In the case of supervised classification, the software system delineates
specific landcover types based on statistical characterization data drawn from
known examples in the image (known as training sites). With unsupervised
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