Resolution
Resolution is a property of an image that describe
the level of detail that can be discerned from it. Since the smallest
element in a satellite image is a single pixel, resolution describes
the area on the Earth's surface represented by a single pixel.
For example, in a weather satellite image that has a resolution
of 1 km, each pixel represents the average brightness value over
an area that is 1 km by 1 km. Features smaller than 1 km will
be difficult to discern clearly in an image with 1 km resolution.
In higher resolution imagery, each pixel represents a much smaller
portion of the Earth. For example, Landsat 7 typically produces
imagery with 30 meter resolution. Thus, each pixel in a Landsat
image represents the average brightness of an area that is 30
meters by 30 meters. Thus, much greater detail can be seen in
a Landsat image when compared to a 1 km weather satellite image
since it has a higher resolution.
[comparison of 3 different resolutions]
The resolution of a particular satellite sensor must be optimized
for the intended use of the data. Weather satellites generally
monitor weather patterns that cover hundreds or even thousands
of miles, therefore there is no need for resolution higher than
about 0.5 km. Landsat and other land-use satellites, however,
need to distinguish between much smaller items, such as a corn
field and a forested area or between a road and a protected wetland.
Therefore, a higher resolution is required. The trade-off for
higher resolution, however, is that the amount of data produced
by the satellite is much greater, which increases transmission
times and burdens the mission with a tremendous amount of data
to store.
Enhancing and Manipulating Image Data
Raw satellite data often contain a vast amount of information
that is not readily apparent to the analyst. Therefore, image
enhancement techniques are used to highlight features of interest
and expose subtle differences in the spectral signature of the
components of the target. Some of these techniques involve modifying
an image in order to improve contrast between features in a well
defined spectral range or to improve resolution and detail, while
other techniques use complex mathematical calculations to derive
an entirely new image from a set of raw image data.
Color and False Color
The human eye can only distinguish between about 16 shades of
gray in an image, however it is able to distinguish between millions
of different colors. Thus, a common image enhancement technique
is to assign specific digital number (DN) values (or ranges of
DN values) to specific colors, thereby increasing the contrast
of particular DN values with the surrounding pixels in an image.
An entire image can be converted from a gray scale to a color
image, or portions of an image that represent the DN values of
interest can be colored.
A true color image is one for which the colors have been assigned
to DN values that represent the actual spectral range of the colors
used in the image (blue features appear blue, green features appear
green, red features appear red, etc). A photograph is an example
of a true color image. The following image is a "true color"
image generated from Landsat 7. The Red pixels in this image are
assigned to pixels that have the strongest spectral response in
the red band of the visible light spectrum, and the same is true
for the green and the blue colors. The result is an image that
most closely represents the Earth's surface as it would appear
to the human eye.
False color is a technique by which colors are assigned to spectral
bands that do not equate to the spectral range of the selected
color. This allows an analyst to highlight particular features
of interest using a color scheme that makes the features stand
out. For example, in the following image, the color red has been
assigned to DN values that are found in the near infrared portion
of the spectrum. Young vegetation, which reflects near infrared,
appears bright red in this image. This image, a 432 composite
image from Landsat 7, is useful for identifying and locating areas
where agricultural activity is concentrated, since new spring
planting is easily detected by its bright red tones.
