GCE Kannur

1. Resolution in Remote Sensing
Refers to the ability of a remote sensing
system to record and display fine details .
Remote sensing image system possess four
major resolution characteristics
1. Spatial Resolution
2. Spectral Resolution
3. Radiometric Resolution
4. Temporal Resolution

2. Spatial Resolution
It is the most important characteristics of a remote
sensing system.
Determine the ability of a remote sensing system in
recording a spatial detail.
Images where only large features are visible are said
to have coarse or low resolution.
In fine or high resolution images, small objects can be
detected. Military sensors for example, are designed
to view as much detail as possible, and therefore
have very fine resolution.

3. Spatial Resolution and Pixel
Most remote sensing images are composed of a matrix of
picture elements, or pixels, which are the smallest units of
an image. Pixels are normally square and represent a
certain area on an image.
It is important to distinguish between pixel size and spatial
resolution. If a sensor has a spatial resolution of 20 m and
an image from that sensor is displayed at full resolution, each
pixel represents an area of 20m x 20m on the ground. In this
case the pixel size and resolution are the same.
However, it is possible to display an image with a pixel size
different than the resolution. Many posters of satellite
images have their pixels averaged to represent larger areas,
although the original spatial resolution of the sensor remains
the same.

4. Spatial Resolution

5. Spectral Resolution
It refers to the electromagnetic
radiation wavelengths to which a
remote sensing system is sensitive.
There are two components.
1.The number of wavelength bands
(or channels) used.
2. Width of each wave band.

6. Spectral Resolution
Spectral resolution describes the ability of a
sensor to define fine wavelength intervals.
A sensor with higher spectral resolution is
required for detailed distinction.
The finer the spectral resolution, the narrower
the wavelength range for a particular
channel or band.

7. Spectral Resolution

8. Spectral Resolution
Different classes of features and
details in an image can often be
distinguished by comparing their
responses over distinct wavelength
ranges. Broad classes, such as water
and vegetation, can usually be
Separated using very broad
wavelength ranges
- the visible and near infrared .
Other more specific classes, such as
different rock types, may not be
Easily distinguishable using these
broad wavelength ranges and would
Require comparison at much finer
wavelength ranges to separate them.

9. Multi-spectral and Hyperspectral Sensors
Many remote sensing systems record energy over several
separate wavelength ranges at various spectral resolutions.
These are referred to as multi-spectral sensors.
Advanced multi-spectral sensors called hyperspectral
sensors, detect hundreds of very narrow spectral bands
throughout the visible, near-IR, and mid-IR portions of the
EM spectrum.
Their very high spectral resolution facilitates fine
discrimination between different targets based on their
spectral response in each of the narrow bands.

10. Radiometric Resolution

12. Temporal Resolution
It is the frequency of data collection.
The revisit period of a satellite sensor is usually several
days. Therefore the absolute temporal resolution of a
remote sensing system to image the exact same area at
the same viewing angle a second time is equal to this
period.
But, because of some degree of overlap in the imaging
swaths of adjacent orbits for most satellites and the
increase in this overlap with increasing latitude, some
areas of the Earth tend to be re-imaged more frequently.

14. Temporal Resolution
Also, some satellite systems
are able to point their sensors
to image the same area
between different satellite paths
separated by periods from one
to five days.
Thus, the actual temporal
resolution of a sensor depends
on a variety of factors, including
the satellite/sensor capabilities,
the swath overlap, and latitude.
Point their sensors to
image the same area

15. Multi-temporal Imagery
The time factor in imaging is important when:
• Persistent clouds offer limited clear views of the
Earth's surface (often in the tropics)
• Short-lived phenomena (floods, oil slicks, etc.) need
to be imaged
• Multi-temporal comparisons are required (e.g. the
spread of a forest disease from one year to the next)
• The changing appearance of a feature over time can
be used to distinguish it from near-similar features
(wheat / maize)

16. Platforms and Sensor
Systems
Remote sensing devices may be operated from a
variety of platforms. These can range from
elevated, but ground-based, platforms such as
tripods and cherry-pickers, through balloons and
aircraft at various altitudes within the Earth's
atmosphere (up to about 100 km) to spacecraft
which operate outside the atmosphere.
The most commonly used platforms for remote
sensing are aircraft and spacecraft.

17. Remote sensing platforms

18. scan angle in aircraft imagery
To obtain an image swath of sufficient width, aircraft
scanners generally use wider scan angles than satellite
scanners. This angle is referred to as the Field of View
(FOV) and may be between 70&90°.
However, image pixel size is determined by a constant angle
of view (known as the Instantaneous Field of View: IFOV).
The effects of panoramic distortion become quite significant
toward the edges of the FOV with the ground pixel size
increasing many times.

19. Effect of scan angle in
aircraft imagery:
(a) Panoramic
distortion: pixel width
increases significantly
away from a vertical view.
(b) Resulting image
distortions: image
features have lateral
distortion when displayed
with a constant pixel width.

21. Sensing systems
Most sensing systems in the visible and infrared regions
of the EM spectrum are passive detectors of reflected
solar radiation or emitted thermal radiation.
Lidar (Laser Imaging raDAR) is an active remote sensing
device which operates in the wavelength range from
ultraviolet to near infrared.
The laser directs pulsed or continuous radiation through
a collimating system while a second optical system
collects the returned radiation and focuses it onto a
detector.

22. Multi-spectral scanners
Multi-spectral scanners (MSS), are a particular class of
remote sensing device which sense radiation in multiple
wavelength regions of the visible, near infrared, middle
infrared and thermal infrared parts of the electromagnetic
spectrum.
As wavelengths in these regions of the spectrum are
strongly affected by atmospheric scattering, the
usefulness of these devices for earth surface studies
may be limited by atmospheric conditions.

23. Scanner operation
Multi-spectral scanners operate in a number of different ways.
They can be grouped into three basic categories depending on
the mechanism used by the sensors to view each pixel.
a. Electromechanical; the sensor oscillates from side
to side to form the image,
b. Linear array; an array of detectors is used to
simultaneously sense the pixel values along a line, and
c. Central perspective; the sensing device does not actually
move, relative to the object being sensed, during image
formation so views all pixels from the same central
position in a similar way to a photographic camera.

24. Sensor records pixels sequentially along each line from line centre.
Electromechanical

25. Image lines are formed sequentially by scanning side-to-side across flight path
Operation of electromechanical aircraft scanner

26. Pixels recorded simultaneously along each line using an array of
detectors at line centre.
Linear array
The scanner does not have
any moving parts to cause
timing inconsistencies and
can allow a longer dwell time,
and hence narrower spectral
channels, per detector. This
results in a cheaper, lighter
and smaller device with lower
power requirements and
greater reliability as well as
higher spatial and
radiometric resolutions.

27. Sensor is positioned at image centre and records lines
sequentially.
Central perspective
This operation can utilise
either electromechanical or
linear array technology to
form image lines but images
each line from a perspective
at the centre of the image
rather than the centre of each
line.
This results in similar
geometric distortions in an
image to those which occur in
photographic data.