Chapter 5

 In order to take advantage of and make good use of remote sensing
data, we must be able to extract meaningful information from the
imagery.
 This brings us to the topic of discussion of interpretation and
analysis.

 Interpretation and analysis of remote sensing imagery involves
the identification and/or measurement of various targets in an
image in order to extract useful information about them.
 Targets in remote sensing images may be any features or
object which can be observed in an image, and have the
following characteristics

 Targets may be a point, line or area.
 This means that they can have any form, from a bus in a parking lot
or plane on a runway, to a bridge or roadway to a large expanse of
water or a field.
 The target must be distinguishable; it must contrast with other
features around it in the image.

 Much interpretation and identification of targets in remote sensing imagery
is performed manually or visually, by a human interpreter.
 In many cases this is done using imagery 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.
 In this case we refer to the data as being in analog format.
 Remote sensing images can also be 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.
 Both analogue and digital imagery can be displayed as black and white
images or as color images.
 When remote sensing data are available in digital format, digital
processing and analysis may be performed using a computer.

 Digital processing may be used to enhance data as a prelude to visual
interpretation.
 Digital processing and analysis may also be carried out to automatically
identify targets and extract information completely without manual
intervention by a human interpreter.
 However, rarely is digital processing and analysis carried out as a complete
replacement for manual interpretation. Often it is done to supplement of
remote sensing for air photo interpretation.
Method Merit Demerit
Human (Image
Interpretation)
• Interpreter’s knowledge are
available
• Excellent in spatial
information extraction
• Time Consuming
• Individual difference
Computer (Image
processing)
• Short processing time
reproductively
• Extraction of physical
quantities or indices is
possible
• Human Knowledge is
unavailable
• Spatial information
extraction is poor.

 Each pixel is characterized for
a brief time by some single
value of radiation (e.g.,
reflectance) converted by the
photoelectric effect into
electrons and then to a number
(see illustration at right)
 The area coverage of the pixel
(that is, the ground cell area it
corresponds to) is determined
by instantaneous field of view
(IFOV) of the sensor system.

 Resolution is defined as a measure of the ability of an
optical system or other system to distinguish between
signals that are spatially near or spectrally similar.

• Spatial - the size of the field-of-view, e.g. 10 x 10 m.
• Spectral - the number and size of spectral regions the sensor
records data in, e.g. blue, green, red, near-infrared, thermal
infrared, microwave (radar).
• Temporal - how often the sensor acquires data, e.g. every
30 days.
• Radiometric - the sensitivity of detectors to small differences in
electromagnetic energy.
• Spatial - the size of the field-of-view, e.g. 10 x 10 m.
• Spectral - the number and size of spectral regions the sensor
records data in, e.g. blue, green, red, near-infrared, thermal
infrared, microwave (radar).
• Temporal - how often the sensor acquires data, e.g. every
30 days.
• Radiometric - the sensitivity of detectors to small differences in
electromagnetic energy.
GG RR NIRNIRBB
10 m10 m
10 m10 m
JanJan
1515
FebFeb
1515

 The fineness of detail visible in an image.
◦ (course) Low resolution – smallest features not discernable
◦ (fine) High resolution – small objects are discernable
 Factors affecting spatial resolution
◦ Atmosphere, haze, smoke, low light, particles or blurred sensor
systems
 Spatial resolution is ability to distinguish between two closely
spaced objects on an image.
 Spatial resolution depends on the field of view (FOV) , altitude and
viewing angle of a sensor.
 The detail discernible in an image is dependent on the spatial
resolution of the sensor and refers to the size of the smallest possible
feature that can be detected.

 Spatial resolution of passive sensors depends primarily on their
instantaneous field of view (IFOV).
 The IFOV is the angular cone of visibility of the sensor and determines
the area on the earth’s surface which is seen from a given altitude at
one particular moment of time.
 The IFOV is the angular cone of visibility of the sensor and determines
the area of visibility of the sensor and determines the area on the earth
surface.
 The IFOV may also be defined as the area on the ground, which viewed
by a single instrument from a given altitude at any given instant of
time.
 This area on the ground is called the resolution cell and determines a
sensor spatial resolution.

 The term spectral resolution refers to the width of
spectral bands that a satellite imaging system can
detect. Often satellite imaging systems are multi-
spectral meaning that they can detect in several discrete
bands, it is the width of these bands that spectral
resolution refers too. The narrower the bands, the
greater the spectral resolution.

 Spectral resolution describes the ability of a
sensor with to define fine wavelength intervals.
 The finer the spectral resolution , the narrower the
wavelength range for a particular channel or band.


 Resolution refers to the dimension spectral and number of
wavelength regions (or band) in the EM spectrum to which the
sensor is sensitive.
 Based on the spectral resolution the sensors fall into the
following groups: Broad-band, Narrow band, spectral and
hyper spectral sensors.
 It uses advanced multichannel sensors.
 Detect hundreds of very narrow spectral bands throughout the
visible near infrared and mid-infrared portions of the EM
spectrum.
 Their very high spectral resolution facilities fine
discrimination between different targets based on their
spectral response in each of the narrow bands.

SpectralSpectral
ResolutionResolution
SpectralSpectral
ResolutionResolution

 Temporal resolution is a measure of how often data are
obtained for the same area (i.e. how often an area can
be revisited)
 The temporal resolution varies from hours for some
systems to about 20 days to others.
 High temporal resolution: Daily or twice daily

June 1, 2006June 1, 2006June 1, 2006June 1, 2006 June 17, 2006June 17, 2006June 17, 2006June 17, 2006 July 3, 2006July 3, 2006July 3, 2006July 3, 2006
Remote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data Acquisition
16 days16 days16 days16 days

 The radiometric resolution of an imaging system
describes its ability to discriminate very slight
difference in energy.
 Radiometric resolution is a measure of the sensitivity
of a sensor to differences in the intensity of the
radiation measured in sensor.
 Radiometric resolution is a measure of how many grey
levels are measured between pure black and pure white.
 The radiometric resolution measured in bit.
 E.g. 1 bit system (2^1=2) only two radiation levels and
2-bit system measures four levels etc.

8-bit8-bit
(0 - 255)(0 - 255)
8-bit8-bit
(0 - 255)(0 - 255)
9-bit9-bit
(0 - 511)(0 - 511)
9-bit9-bit
(0 - 511)(0 - 511)
0
0
0
7-bit7-bit
(0 - 127)(0 - 127)
7-bit7-bit
(0 - 127)(0 - 127)0
10-bit10-bit
(0 - 1023)(0 - 1023)
10-bit10-bit
(0 - 1023)(0 - 1023)

 Radiometric resolution, or radiometric sensitivity
refers to the number of digital levels used to express
the data collected by the sensor. In general, the greater
the number of levels, the greater the detail of
information.


 Suppose you have a digital
image which has a
radiometric resolution of 6
bits. What is the maximum
value of the digital number
which could be represented
in that image?

 The number of digital values possible in an
image is equal to the number two (2 - for
binary coding in a computer) raised to the
exponent of the number of bits in the image.
The number of values in a 6-bit image
would be equal to 26
= 2 x 2 x 2 x 2 x 2 x 2 =
64. Since the range of values displayed in a
digital image normally starts at zero (0), in
order to have 64 values, the maximum value
possible would be 63.

 The radiometric
resolution of an
imaging system
describes its ability to
discriminate very slight
differences in energy
The finer the
radiometric resolution
of a sensor, the more
sensitive it is to
detecting small
differences in reflected
or emitted energy.

 Analysis of remote sensing imagery involves the identification
of various targets in an image and those targets may be
environmental or artificial features which consists of points,
lines and areas.
 Targets may be defined in terms of the way they reflect or
emit radiation.
 This radiation is measured and recorded by a sensor and
ultimately is depicted as an image product such as an air photo
or a satellite image.
 Recognizing targets is the key to interpretation and
information extraction. Basic elements of image interpretation
are:

 Tone
 Shape
 Size
 Pattern
 Texture
 Shadow
 Site, Situation and Association

 It refers to the relative brightness or color of objects in
an image.
 Generally, tone is the fundamental element for
distinguishing between different targets or features.
 Variations in tone also allows the elements of shape,
texture, and pattern of objects to be distinguished.

 It refers to the general form, structure, outline of
individual objects.
 Shape can be very distinctive clue for interpretation
 Straight edge shape typically represents urban or
agricultural targets, while natural features such as
forest edge more irregular in shape, except where man
has created a road or clear cuts.
 Farm or crop land irrigated systems would appear as
circular shape.

 Size of objects in an image is a function of scale
 It is important to assess the size of a target relative to
other objects in a scene as well as the absolute size, to
aid in the interpretation of that target.
 A quick approximation of target size can direct
interpretation to an approximate result more quickly.
 For e.g. if an interpreter had to distinguish zones of
land use and had identified an area with a number of
buildings in it, large buildings such as factories or
warehouses would suggest commercial property,
whereas small buildings would indicate residential use.

 Pattern refers to the spatial arrangement of visibility
discernible (detectable) objects.
 Typically an orderly repetition of similar tones and
textures will produce a distinctive and ultimately
recognizable pattern.
 Orchards with evenly spaced trees and urban streets
with regularly spaced houses are good examples of
pattern.

 It refers to the arrangement and frequency of tonal
variations in particular areas of an image.
 Rough texture would consists of mottled tone where
gray levels change abruptly in small area, where as
smooth texture would have very little tonal variation.
 Smooth texture are most often the result of uniform,
even surfaces, such as field or grass lands.
 A target with a rough surface and irregular structure
such as forest canopy, results in a rough textured
appearance.
 Texture is one of most important element for
distinguishing features in RADAR imagery.

 It is also very helpful in interpretation as it may provide
an idea of profile and relative height of target which
may make identification easier.
 However, shadows can also reduce or eliminate
interpretation in their area of influence, since target
within shadow are much less detectable from their
surrounding.
 Shadow is also helpful for enhancing or identifying
topography and landform, particularly in RADAR
imagery.

 It takes into account the relationship between other
recognizable objects or features in proximity to the
target of interest.
 The identification of features that one would expect to
associate with other features may provide information
to facilitate identification.
 For example, commercial properties may be associated
with proximity to the major transportation routes,
where as residential areas would be associated with
schools, playgrounds, sport field. Other example a lake
is associated with boats, a marine and adjacent
recreational land.

 The criteria of image interpretation is as follows:
 Image reading is an element in the form image interpretation.
It corresponds to simple identification of objects using such
elements as shape, size, pattern, tone, texture, color, shadow
and other associated relationship.
 Image measurement is the extraction of physical quantities
such as length, height, location, density, temperature and so
on, by using reference data or calibration data.
 Image analysis is the understanding of the relationship
between interpreted information and the actual status or
phenomenon and to evaluate the situation.
 The image interpretation process is clearly shown in figure
below.

Figure: Image Interpretation process