After data is collected and transmitted to the ground
station, it must be processed and converted into a format that is
usable by the researcher who will interpret the data. Often satellite-derived
data is converted into imagery that provides a visualization of
the data collected by the sensor. However, the format of these data
in their original form is usually not such that an interpreter can
learn much about the target. Often, the data must be processed,
enhanced, and manipulated to provide a useful set of information.
This technique, which is part science and part art, is called image
processing.
Satellite image data is sent from the satellite to
the ground station in a raw digital format, which is essentially
a stream of numerical data. The smallest unit of digital data is
a bit. A bit is represented by a binary number, which has only two
possible values, 0 or 1. A bit can be used to represent any piece
of data that has two states, such as on/off, true/false, or open/closed.
With only two potential values, a bit does not offer much flexibility
in representing data that is more complex than a binary number.
Therefore, data is often stored as a collection of eight bits, resulting
in a unit of data called a byte.
A byte is a unit of data that is comprised of 8 bits,
thus providing a data element with up to 256 potential values (2^8).
Radiometers that measure the intensity of electromagnetic radiation
will generally convert the detected energy levels into a value that
ranges from 0 to 255 and represent each of these measured energy
levels with a single byte. These bytes will be strung together in
a pre-determined manner, converted into a signal, and transmitted
to the collections facility. Here, the signal will be converted
back into a digital stream of bytes where it can be read in and
interpreted by processing software. Images generated in this manner
are thus referred to as "8-bit digital images."
Though remotely-sensed images are collected from a
wide variety of sensors and transmitted to a ground station through
many different paths, all image data have certain characteristics
in common.
When a stream of bytes is received from a satellite
sensor, the value of each byte is applied to a single dot, or pixel
(short for "picture element"). The numerical value of
the pixel, known as its Digital Number (DN), is translated into
a shade of gray that ranges somewhere between white and black. These
pixels, when arranged together in the correct order, form an image
of the target in which the varying shades of gray represent the
varying energy levels detected on the target.
The following image illustrates this concept. The
Landsat 7 image clip in the upper left of this image is a false
color composite image centered over a resevoir in a portion of central
Maryland. When a selected portion of the image is magnified several
times, it becomes apparent that the image is really just comprised
of rows of pixels, each with its own color.
It is important to remember that a satellite image
is not just a picture of the target similar to what a simple camera
would take. Instead it is a collection of numeric data that is capable
of being displayed as an image. The underlying dataset can be manipulated
using algorithms (mathematical equations) that correct for errors
(like atmospheric interference), re-map the data to a geographical
reference point, or extract information that is not readily apparent
in the data. The data for two or more images of the same location
can even be combined mathematically, creating imagery that is a
composite of multiple datasets. These data products, known as derived
products, can be generated by performing calculations on the raw
numerical (digital numbers) data.
Gray scale
Most raw unprocessed satellite imagery is stored in a gray scale
format. A gray scale is a color scale that ranges from black to
white, with varying intermediate shades of gray. A commonly used
gray scale for remote sensing image processing is a 256 shade
gray scale, where a value of 0 represents a pure black color,
the value of 255 represents pure white, and each value in between
represents a progressively darker shade of gray.
|
[256 level gray scale]
|
Objects in a gray tone display have a brightness value (or digital
number), which represents the measured energy level of the item.
Contrast refers to the difference in relative brightness between
an item and its surroundings as seen in the image. A particular
feature is easily detected in an image when contrast between an
item and its background are high. However, when the contrast is
low, an item might go undetected in an image.
The following two images illustrate differences in contrast between
images taken in different portions of the electromagnetic spectrum.
The image on the left is a clip from a Landsat 7 Thematic Mapper
Band 4 image, which depicts the spectral response in the near
infrared portion of the spectrum. This spectral band is especially
sensitive to young vegetative growth, which contains pigments
that reflect near infrared radiation from its leaf surfaces. The
image on the right is a clip from the exact same scene as the
first image and is a Landsat 7 Thematic Mapper Band 3 image, which
depicts the spectral response in the visible red portion of the
spectrum. This channel is not very sensitive to vegetation. In
both images, contrast between the deep reservoir water (which
appears black in the lower center part of each image) and land
is relatively high. In the first image, however, the contrast
between the agricultural fields (which are bright white and light
gray) and the surrounding land use classes (pasture, suburban
developments, forested areas) is much higher than in the visible
red image. This is caused by the heightened sensitivity to reflected
near infrared in the channel 4 sensor.
Thematic Mapper Band
4 (RED)
|
Thematic Mapper Band 3
(NEAR IR)
|
Images of raw, unprocessed data streams are often not particularly
useful to a human interpreter, since the contrast is often very
low and the human eye can only distinguish between a few dozen
shades of gray. Image processing techniques can be used to enhance
the contrast between the most important shades of gray that make
up an unprocessed image.
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.
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.
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