2. Topics to be covered
• Digital data
• Radiometric Characteristics of Image Data
• Geometric Characteristics of Image Data
• Data formats
• Image Processing in Remote Sensing
3. Analog vs digital
• Images with a continuous gray tone or color,
like a photograph are called analog images
• On the other hand, a group of divided small
cells, with integer values of average intensity,
the center representing the cell's value, is
called a digital image
4.
5. Digital data
• A digital image comprises of a two
dimensional array of individual picture
elements called pixels arranged in columns
and rows.
• Each pixel represents an area on the Earth's
surface.
• A pixel has an intensity value and a location
address in the two dimensional image.
6. Radiometric characteristics of Image Data
• The intensity value represents the measured
physical quantity such as the solar radiance in
a given wavelength band reflected from the
ground.
• This value is normally the average value for
the whole ground area covered by the pixel.
7. Radiometric characteristics of Image Data
• The intensity of a pixel is digitised and
recorded as a digital number.
• Due to the finite storage capacity, a digital
number is stored with a finite number of bits
(binary digits).
• For example, an 8-bit digital number ranges
from 0 to 255 (i.e. 28 - 1)
8. Geometric Characteristics of Image Data
• IFOV (Instantaneous Field Of View) is defined
as the angle which corresponds to the
sampling unit
• Information within an IFOV is represented by a
pixel in the image plane.
• The maximum angle of view which a sensor
can effectively detect the electromagnetic
energy, is called the FOV (Field of View).
• The width on the ground corresponding to the
FOV is called the swath width.
10. Digital Image Processing in Remote
Sensing
• Preprocessing
• Image Enhancement
• Image Transformation
• Image Classification and analysis
11. Preprocessing
• Involves those operations that are normally required prior to
the main data analysis and extraction of information.
• Radiometric and geometric corrections.
• Radiometric corrections include correcting the data for sensor
irregularities and unwanted sensor or atmospheric noise, and
converting the data so that they accurately represent the
reflected energy measured by the sensor.
• Geometric corrections include correcting for geometric
distortions due to sensor-Earth geometry variations and
conversion of the data to real world coordinates (e.g., latitude
and longitude) on the Earth’s surface.
12. Image enhancement
• Image enhancement is the process by which
the appearance of the imagery is improved so
as to assist in a better way for visual
interpretation and analysis.
• Examples of image enhancement functions -
Contrast stretching to increase the tonal
distinction between various features in an
image, Spatial filtering to enhance or suppress
specific spatial patterns in an image.
13. Image transformations
• Operations similar to that of image enhancement.
• But, unlike image enhancement functions which are applied
only to a single band of data at a time, image transformation
operations usually involve multiple spectral bands.
• Arithmetic operations (subtraction, addition, multiplication,
division) are performed to combine and transform the original
bands into “new” images which better display or highlight
certain features in an image.
• Examples are spectral ratioing, principal components analysis
etc.
14. Image classification and analysis
• Image classification and analysis operations are used
to digitally identify and classify pixels in the data.
• Classification is usually performed with multi-band
data sets and this process assigns each pixel in an
image to a particular class of theme based on
statistical characteristics of the pixel brightness
values.
• The two methods of digital image classification are
supervised and unsupervised classification
15. Supervised Classification
• In supervised classification, we identify examples of the
Information classes (i.e., land cover type) of interest in the
image. These are called "training sites".
• The image processing software system is then used to develop
a statistical characterization of the reflectance for each
information class.
• This stage is often called "signature analysis" and may involve
developing a characterization as simple as the mean or the
range of reflectance on each bands, or as complex as detailed
analyses of the mean, variances and covariance over all
bands.
16. Supervised Classification
• Once a statistical characterization has been
achieved for each information class, the image
is then classified by examining the reflectance
for each pixel and making a decision about
which of the signatures it resembles most
based on suitable classifier algorithm
17. Unsupervised classification
• The goal of unsupervised classification is to
automatically segregate pixels of a satellite image
into groups of similar spectral character.
• Classification is done using one of several statistical
routines generally called "clustering" where classes
of pixels are created based on their shared spectral
signatures.
• Clusters are split and /or merged until further
clustering doesn't improve the explanation of the
variation in the scene.
18. • No extensive prior knowledge of the region that is
required
• The opportunity for human error is minimized because the
operator may specify only the number of categories desired
and sometimes constraints governing the distinctness and
uniformity of groups.
• Many of the detailed decisions required for supervised
classification are not required for unsupervised classification
creating less opportunity for the operator to make errors.
• Unsupervised classification allows unique classes to be
recognized as distinct units.
Unsupervised classification
