groundtruth collection for remotesensing support

1. Ground Truth Collection
For
Remote Sensing Support
Presented by :
P.Thiruvengadam
M.Tech Geoinformatics

2. Remote Sensing
Definition:
The science and art of obtaining information about an object,
area, or phenomenon through the analysis of data acquired by
a device that is not in contact with the object, area, or
phenomenon under investigation (L&K,1994)
Example : Human Visual system

3. Principles Of Remote sensing
• Detecting and recording of radiant energy reflected or emitted
by objects or surface material
• Different objects return different amount of energy in different
bands of the electromagnetic spectrum, incident upon it.
• Reflection or emmitance of electromagnetic spectrum depends
on
• Property of material (structural, chemical, and physical).
• Surface roughness.
• Angle of incidence.
• Intensity.
• Wavelength of radiant energy.

4. Stages In Remote Sensing

5. Electromagnetic Energy
• Energy propagated in the form of an advancing interaction
between electric and magnetic fields (Sabbins, 1978)
• The electromagnetic is normally used as an information carrier
in remote sensing.
• Travels with the velocity of light.
• The electromagnetic spectrum ranges from the shorter
wavelengths (including gamma and x rays) to the longer
wavelengths (including microwaves and broadcast radio
waves)
• Most of the remote sensing systems operate in visible, infrared
(IR) and microwave regions

6. • Visible region
EMR Spectrum
‒ Small region in the range 0.4 - 0.7
μm
‒ Blue : 0.4 – 0.5 μm
‒ Green: 0.5-0.6 μm
‒ Red: 0.6-0.7 μm.
‒ Ultraviolet (UV) region adjoins the
blue end
‒ Infrared (IR) region adjoins the red
end
• Microwave region
‒ Longer wavelength intervals
‒ Ranges from 0.1 to 100 cm
‒ Includes all the intervals used
by radar systems.
 Infrared (IR) region
‒ Spanning between 0.7 and 100 μm
‒ 4 subintervals of interest for remote
sensing
(1) Reflected IR (0.7 - 3.0 μm)
(2) Photographic IR (0.7 - 0.9 μm)
(3) Thermal IR at 3 - 5 μm
(4) Thermal IR at 8 - 14 μm

7. Remote Sensing Platforms
• Ground level remote sensing
– Very close to the ground (e.g., Hand held
camera)
– Used to develop and calibrate sensors
for different features on the Earth’s
surface
• Aerial remote sensing
– Low altitude aerial remote sensing
– High altitude aerial remote sensing
• Space-borne remote sensing
– Space shuttles
– Polar orbiting satellites
– Geo-stationary satellites

8. Real Remote Sensing System
Energy Source
• Ideal system: Constant, high level of output over all wavelengths
• Real system:
 Usually non-uniform over various wavelengths
 Energy output vary with time and space
 Affects the passive remote sensing systems
– The spectral distribution of reflected sunlight varies both temporally and spatially
– Earth surface features also emit energy in varying degrees of efficiency
 A real remote sensing system needs calibration for source characteristics.

9. The Atmosphere
Ideal system: A non-interfering atmosphere
Real system:
 Atmosphere modifies the spectral
distribution and strength of the energy
transmitted through it
 The effect of atmospheric interaction
varies with the wavelength associated,
sensor used and the sensing application
 Calibration is required to eliminate or
compensate these atmospheric effects

10. The Energy/Matter Interactions at the Earth's Surface
• Ideal system: A series of unique energy/matter interactions
• Real system:
 Spectral signatures may be similar for different material, making the differentiation
difficult
 Lack of complete understanding of the energy/matter interactions for surface features
The Sensor
• Ideal system: A super sensor
• Real system:
 Fixed limits of spectral sensitivity i.e., they are not sensitive to all wavelengths.
 Limited spatial resolution (efficiency in recording spatial details).
 Sensor selection requires a trade-off between spatial resolution and spectral sensitivity.
– For example, photographic systems have very good spatial resolution , but poor
spectral sensitivity. Non-photographic systems have poor spatial resolution.

11. Advantages of Remote Sensing
• Major advantages of remote sensing are
 Provides data for large areas
 Provide data of very remote and inaccessible regions
 Able to obtain imagery of any area over a continuous period of time
– Possible to monitor any anthropogenic or natural changes in the
landscape
 Relatively inexpensive when compared to employing a team of
surveyors
 Easy and rapid collection of data
 Rapid production of maps for interpretation

12. Limitations of Remote Sensing
• Some of the drawbacks of remote sensing are
 The interpretation of imagery requires a certain skill level
 Needs cross verification with ground (field) survey data
 Data from multiple sources may create confusion
 Objects can be misclassified or confused
 Distortions may occur in an image due to the relative motion of sensor
and source

13. Ground Truth
• Ground truth is simply observations or measurements made at
or near the surface of the earth in support of an air or space-based
remote sensing survey (UCSC).
• It may also be referred to as ancillary data or reference data.
• Ground truth may consist of several types of data acquired
before, during, and after an image acquisition

14. Sources of Ground Truth
• Field observations.
• In situ spectral measurements.
• Aerial reconnaissance and photography.
• Descriptive reports.
• Inventory tallies.
• Maps

15. Examples of Ground Truth
• Field-based spectroscopic measurements of representative
materials of interest such as trees, shrubs, rock outcrops, soils,
water, and manmade objects such as roads and buildings
• Meteorological conditions at time of over flight (including
temperature, wind speed and direction, precipitation, etc).
• Chemical and biochemical measurements such as chlorophyll
content of tree canopies or chemical constituent identification
within waterways.

16. • Rock and mineral analysis and collection including such things
as permeability tests, grain size estimates, and simple hand
sample identification
• Field mapping of both biological and geological materials and
their distributions, including gross species and lithological
identification
• Collection of previous work such as maps other chemical
analyses, GIS information and any other remotely sensed data

17. Ground Truth Collection for RS
• Correlate surface features and localities as known from
familiar ground perspectives with their expression in satellite
imagery
• Provide input and control during the first stages of planning
for analysis, interpretation, and application of remote sensing
data (landmark identification, logistics of access. etc.)
• Reduce data and sampling requirements (e.g., areas of needed
coverage) for exploration, monitoring, and inventory activities

18. •Select test areas for aircraft and other multistage support
missions (e.g., under flights simultaneous with spacecraft passes)
•Identify classes established by unsupervised classification
•Select and categorize training sites for supervised classification
•Verify accuracy of classification (error types and rates) by using
quantitative statistical techniques
•Obtain quantitative estimates relevant to class distributions (e.g.
field size; forest acreage)

19. • Collect physical samples for laboratory analysis of phenomena
detected from remote sensing data (e.g., water quality; rock
types; insect-induced disease)
• Acquire supplementary (ancillary) non-remote sensing data for
interpretive model analysis or for integration into Geographic
Information Systems
• Develop standard sets of spectral signatures by using ground-based
instruments
• Measure spectral and other physical properties needed to
stipulate characteristics and parameters pertinent to designing
new sensor systems

20. Source:http://ces.iisc.ernet.in/

21. Multi Approach
Ground truth activities are an integral part of the “Multi"
approach:
• Multistage: Data should be procured whenever possible from
different platforms
• Multilevel : Data collected at various distances from Earth’s
surface.
• Multispectral : Data collected over various regions of the
spectrum.
• Multi temporal :Data obtained at different times.
• Multi phase: some data correlate with one another and also
with other remote sensing data.