chapter 5: image interpretation Flashcards
Satellite imagery can be in one of the following two formats:
Analog and digital
Analog:
which data is displayed in a pictorial or photograph‐type format, independent of what type of sensor was used to collect the data and how the data were collected
interpretation and identification of targets in this imagery is performed manually or visually, ie by human interpreter
Digital
data is represented in a computer as arrays of pixels, with each pixel corresponding to a digital number, representing the brightness level of that pixel in the image
When remote sensing data are available in digital format,digital processing and analysismay be performed using a computer.
Both analogue and digital imagery can be displayed as
black and white (also called monochrome) images, or as
color images by combining different channels or bands representing different wavelengths.
Visible Imagery (VIS)
Images obtained using reflected sunlight at visible wavelengths, 0.4 to 0.7 um
Visible imagery is displayed in such a way that:
- high reflectance objects, e.g. dense cirrus from CB clusters, fresh snow, nimbostratus etc., are displayed as white, and
- low reflectance objects, e.g. much of the earth’s surface, is black
There are grey shades to indicate
different levels of albedo (or reflectivity)
Visible imagery is not available
at night
Infra Red (IR)These images are obtained by measuring
the intensity of the thermal emissions from the earth and the atmosphere, at IR wavelengths in the range 10‐12 um
This so‐called ‘window’ need to be
chosen to allow the satellite sensors to detect such radiation unhindered, and the 10‐12 um band is one such.
For example, the GOES (8‐11) sensors use
the atmospheric infrared window centered at 10.7 micrometers
For example, the GOES (8‐11) sensors use the atmospheric infrared window
centered at 10.7 micrometers
at this wavelenght
energy radiated by the earth’s surface and clouds is not significantly attenuated by atmospheric gases.
For example, the GOES (8‐11) sensors use the atmospheric infrared window centered at 10.7 micrometers.
At this wavelength, energy radiated by the earth’s surface and clouds is not significantly attenuated by atmospheric gases
in this channel
most surfaces and cloud types have an emissivity close to 1.0, with a notable exception being thin cirrus.
For example, the GOES (8‐11) sensors use the atmospheric infrared window centered at 10.7 micrometers.
In this channel, most surfaces and cloud types have an emissivity close to 1.0, with a notable exception being thin cirrus.
therefore
the brightness temperature sensed by the satellite is close to actual surface skin or cloud top temperature for other scenes.
IR imagery is so presented that warm/high intensity emissions are
dark grey or even black
IR imagery is so presented that warm/high intensity emissions are dark grey or even black, and low intensity/cold emissions are
white
IR imagery is so presented that warm/high intensity emissions are dark grey or even black, and low intensity/cold emissions are white. This convention was chosen so that the output would correspond with that from
the VIS channels
Color slicing is also frequently used whereby
different colors are assigned to various temperature ranges, thus rendering the cooling/warming of cloud tops (and thus the development/decay) easy to appreciate:
- warming/darkening of the imagery with time indicates descent and decay
- cooling/whitening images imply ascent and development
Water Vapor (WV)
This imagery is derived from
emissions in the atmosphere clustered around a wavelength of 6.7 um
The infrared water vapor channel on board GOES‐8to‐11 is located at 6.7 micrometers where the earth’s
emitted spectrum is highly attenuated by water molecules. Thus, this channel senses radiation from the mid‐and upper‐ levels of the atmosphere, from both water vapor and clouds.
IR channel, this wavelength undergoes
strong absorption by WV in the atmosphere (i.e. this isnota ‘window’), and so can also be used to infer vertical distribution and concentration of WV ‐ an important atmospheric constituent
WV imagery uses
the radiation absorbed and re‐emitted by water vapor in the troposphere.
If the upper troposphere is moist
WV emissions will be dominated by radiance from these higher levels is conventionally shown white.
If the upper troposphere is dry
then the sum of the radiation is biased towards lower altitude WV bands: and this is displayed as a shade of grey, or even black.
Because water vapor is transported by
atmospheric circulations
Because water vapor is transported by atmospheric circulations, it allows
the detection of features in the mesoscale flow as well as hemispheric patterns
WV imagery is also very important in the study of
cyclogenesis, often being displayed as a time‐sequence.
Near‐IR (Shortwave IR)
Imagery from a specific wavelength of 3.9um, lies in the overlap region of the electromagnetic (EM) spectrum betweensolar and terrestrial radiation.
Near‐IR (Shortwave IR)
Radiation in this wavelength region is
not significantly attenuated by the earth’s atmosphere.
Near‐IR (Shortwave IR)
these images uses
a mixture of reflected solar radiation plus radiation emitted by the earth and atmosphere.
Near‐IR (Shortwave IR)
it is used in
fog/very low cloud studies
Near‐IR (Shortwave IR)
It is used in fog/very low cloud studies. Interpretation is
sometimes complex, especially in the presence of other tropospheric clouds.
Enhancements are used to
improve the appearance of the imagery and assist in visual interpretation and analysis.
In raw imagery, the useful data often populates
only a small portion of the available range of digital values (commonly 8 bits or 256 levels).
Contrast enhancement involves
changing the original values so that more of the available range is used, thereby increasing the contrast between targets and their backgrounds.
Understanding contrast enhancements requires the concept of
an image histogram
A histogram is
a graphical representation of the brightness values that comprise an image. The brightness values (i.e. 0‐255) are displayed along the x‐axis of the graph. The frequency of occurrence of each of these values in the image is shown on the y‐axis
By manipulating the range of digital values in an image, graphically represented by its histogram, we can apply
various enhancements to the data
There are many techniques of enhancing contrast and detail in an image; the simplest type of enhancement is a
linear contrast stretch
Linear Contrast Stretch
This involves identifying lower and upper bounds from the histogram (usually the minimum and maximum brightness values in the image) and applying a transformation to stretch this range to fill the full range
In our example, the minimum value in the histogram is 84 and the maximum value is 153. These 70 levels occupy
less than one‐third of the full 256 levels available. A linear stretch uniformly expands this small range to cover the full range of values from 0 to 255.
In our example, the minimum value in the histogram is 84 and the maximum value is 153. These 70 levels occupy less than one‐third of the full 256 levels available. A linear stretch uniformly expands this small range to cover the full range of values from 0 to 255.
This enhances
the contrast in the image with light toned areas appearing lighter and dark areas appearing darker, making visual interpretation much easier
Histogram‐Equalized Stretch
A uniform distribution of the input range of values across the full range may not always be an appropriate enhancement, particularly if the input range is not uniformly distributed. In this case, ahistogram‐equalized stretchmay be better
(histogram equalized stretch)
This stretch assigns
more display values (range) to the frequently occurring portions of the histogram.
This stretch assigns more display values (range) to the frequently occurring portions of the histogram.
• In this way, the detail in these areas will be
better enhanced relative to those areas of the original histogram where values occur less frequently.
This stretch assigns more display values (range) to the frequently occurring portions of the histogram.
• In this way, the detail in these areas will be better enhanced relative to those areas of the original histogram where values occur less frequently.
• In other cases, it may be desirable to
enhance the contrast in only a specific portion of the histogram.
For example, suppose we have an image of the clouds, that occupy the digital values from 40 to 76 out of the entire image histogram. If we wish to enhance the detail in the clouds, we could
stretch only that small portion of the histogram represented by the clouds (40 to 76) to the full grey level range (0 to 255).
For example, suppose we have an image of the clouds, that occupy the digital values from 40 to 76 out of the entire image histogram. If we wish to enhance the detail in the clouds, we could stretch only that small portion of the histogram represented by the clouds (40 to 76) to the full grey level range (0 to 255). All pixels below or above these values would be assigned to
0 and 255
For example, suppose we have an image of the clouds, that occupy the digital
values from 40 to 76 out of the entire image histogram. If we wish to enhance the
detail in the clouds, we could stretch only that small portion of the histogram
represented by the clouds (40 to 76) to the full grey level range (0 to 255). All
pixels below or above these values would be assigned to 0 and 255, respectively,
and the detail in these areas would
be lost
For example, suppose we have an image of the clouds, that occupy the digital values from 40 to 76 out of the entire image histogram. If we wish to enhance the detail in the clouds, we could stretch only that small portion of the histogram represented by the clouds (40 to 76) to the full grey level range (0 to 255). All pixels below or above these values would be assigned to 0 and 255, the details in the clouds would be
greatly enhanced
the purpose of a meteorological satellite image interpretation is to
relate atmospheric features on an image to physical processes
the purpose of a meteorological satellite image interpretation is to relate atmospheric features on an image to physical processes. image interpretation can help
in identifying the mechanism
list some of the atmospheric features that are commonly identified on meteorological satellite imegery:
- inter tropical convergence zone
- severe thunderstorms and squall lines
- hurricane structure
- frontal systems
- jet streams
*
the ITCZ is
an easily recognizable, intermittent band of cloudiness that circles the Earth in the vicinity of the equator