Enhancement curves
When the brightness value and corresponding gray tone of each
pixel in a gray tone display is plotted on a graph, the line that
results illustrates the relationship between brightness and gray
tone. This relationship is a linear relationship that distributes
gray scale tones evenly across the 256 potential brightness values
in the satellite image data.
The problem with this linear relationship is that the brightness
values of interest may be concentrated in a small portion of the
brightness value range, and the gray tones assigned to the brightness
values outside of the area of interest are essentially wasted.
To improve the contrast in the portion of the image that is of
interest we can use mathematically defined enhancement curves.
These curves are a commonly used tool in processing remotely sensed
imagery since they re-distribute the most frequently used gray
scale values in such a way as to highlight a particular range
or gradient of brightness values that is of use to the particular
needs of the researcher.
The image below illustrates the use of a simple enhancement
curve that re-distributes the gray scale values to highlight
the brightness values in the upper middle range. Enhancement curves
can be much more complex than this simple example, and they are
often developed to be applied to imagery for specific purposes.
Transformation
A transformation is a image which is created
by transforming raw image data into an entirely new image using
mathematical formulas (or algorithms) to calculate a new digital
number for each pixel in an image. The underlying numerical data
that makes up the raw satellite data is changed, or transformed,
into another format according to a set of established mathematical
rules.
Composite images
A composite image is a transformation derived
from two or more images of the same geographical region taken
in different bands of the electromagnetic spectrum. Each pixel
value in each of the images is extracted and a calculation is
performed that produces a computed value. The entire set of computed
values is then stored as a new image and can be displayed in a
gray scale or color display the same way a non-derived image can
be viewed. This derived product may provide insights into the
complex relationships between the data in each participating image
that would be otherwise undetectable from the individual images.
Composite images are especially useful when used with multispectral
data, such as that produced by the Landsat satellite. Images from
each of the 7 different Landsat sensors can be combined to create
composite data products that collectively provide far more insight
into the nature of the target than the 7 individual image datasets.
Classification
Classification is a process by which a set of items is grouped
into classes based on common characteristics. Classification of
satellite image data is based on placing pixels with similar values
into groups and identifying the common characteristics of the
items represented by these pixels.
Classification is another tool that is very useful when multispectral
imagery of the same geographical region is compared. Algorithms
can be used that derive a value for each pixel in the image from
its brightness values in each image. Plotting the resulting data
on a 2 or 3 dimensional graph can identify clusters of pixels
that share common spectral characteristics across multiple bands.
Preprocessing
Before digital images can be analyzed, they usually require some
degree of preprocessing. This may involve radiometric corrections,
which attempt to remove the effects of sensor errors and/or environmental
factors. Common corrections of this type include those that attempt
to adjust DN values that have been affected by atmospheric interference
or absorption. Ancillary data collected at the same time the image
was obtained can be used as a calibration tool to support radiometric
corrections.
Geometric Corrections are also a very important form of
pre-processing. This method attempts to rectify any error introduced
into an image by the geometry of the curved Earth's surface and
the movement of the satellite. Geometric correction is a process
by which points in an image are registered to corresponding points
on a map or other image that has already been rectified. The goal
of geometric correction is to put image elements in their proper
planimetric (x and y) positions.
Resources
- NASA
Observatorium Education-Reference Module: Digital Data
- http://observe.ivv.nasa.gov/nasa/education/reference/digital/one.html
- NASA
Observatorium Education-Reference Module: Spatial Resolution
- http://observe.ivv.nasa.gov/nasa/education/reference/resolve/resolve.html
- NASA
Observatorium Education-Reference Module: False Color
- http://observe.ivv.nasa.gov/nasa/education/reference/false/fascol.html
