Ground Truth CollectionFor

Remote Sensing Support

Presented by :

P.Thiruvengadam

M.Tech Geoinformatics

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

Remote Sensing

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.

Stages In Remote Sensing

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

• Visible region

‒ 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

EMR Spectrum

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

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.

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

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.

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

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

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

Sources of Ground Truth

• Field observations.

• In situ spectral measurements.

• Aerial reconnaissance and photography.

• Descriptive reports.

• Inventory tallies.

• Maps

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.

• 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

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

•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)

• Collect physical samples for laboratory analysis of phenomenadetected from remote sensing data (e.g., water quality; rocktypes; insect-induced disease)

• Acquire supplementary (ancillary) non-remote sensing data forinterpretive model analysis or for integration into GeographicInformation Systems

• Develop standard sets of spectral signatures by using ground-based instruments

• Measure spectral and other physical properties needed tostipulate characteristics and parameters pertinent to designingnew sensor systems

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

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.