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Appendix III: Ten (10) Specialty Areas - Remote Sensing/Imagry Science
Curriculum Mapping to Knowledge Units-RS/Imagry Science Specialty Area
III. Remote Sensing/Imagery Science Specialty Area
1. Knowledge Unit title: Remote Sensing Collection Platforms
A. Knowledge Unit description and objective: Understand and be familiar with remote
sensing collection platforms and apply this knowledge to solving spatio-temporal problems
B. Requirement satisfaction: This KU is satisfied when seven (7) Topics and all Learning
Objectives are met.
Remote Sensing Collection Platforms
COURSE/CLASS
ID
III.1C1
III.1C2
TOPICS
Fundamentals of remote sensing platforms
High and low altitude airborne remote sensing
platforms
Platform/sensor position/orientation measurement
III.1C3
Effects of airborne sensor flight path, speed and
control parameters on data collection
III.1C4
Airborne remote sensing data applications
III.1C5
III.1C6
III.1C7
Space-borne remote sensing platforms and sensors
Imaging satellite orbits (e.g. geosynchronous, sun
synchronous, etc.)
Space-borne remote sensing data applications
III.1C8
III.1C9
III.1C10
III.1C11
Un-manned aerial vehicles (UAV), remotely piloted
aircraft (RPA), unmanned aircraft systems (UAS)
remote sensing platforms
UAV/RPA/UAS mission planning
UAV/RPA/UAS data collection and processing
UAV/RPA/UAS remote sensing applications
III.1C12
III.1D1
III.1D2
III.1D3
LEARNING OBJECTIVES
Define the basic theories, processes of remote sensing
platforms and sensors
Define the common applications for data from remote
sensing platforms/sensors
Describe the effects and parameters on imagery in
relation to airborne sensor flight path, speed and
control (pitch, yaw, roll)
Discuss airborne remote sensing platform applications
III.1D4
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Remote Sensing Collection Platforms
COURSE/CLASS
ID
III.1D5
III.1D6
III.1D7
LEARNING OBJECTIVES
Describe the current constellation of space-borne
remote sensing platforms in terms of their
technologies, orbital parameter capabilities and their
applications
Describe the difference between nadir-looking and
"agile" satellites (i.e., Worldview-2)
Describe UAV/RPS/UAS remote sensing data
collection and their applications
2. Knowledge Unit title: Radiometry
A. Knowledge Unit description and objective: Skills and knowledge required to
comprehend the quantitative measurement of electromagnetic energy and its application to simple
imaging systems
B. Requirement satisfaction: This KU is satisfied when all Topics and all Learning Objectives
are met.
Radiometry
COURSE/CLASS
ID
TOPICS
Radiometric and photometric quantities (SI units)
III.2C1
III.2C2
Quantitative measurement of electromagnetic energy
and its application to imaging sensors
Optical properties of materials and components
III.2C3
III.2C4
III.2C5
Ways to characterize radiometric performance of
detectors
Radiometric calibration/normalization principles,
approaches and tools
LEARNING OBJECTIVES
Define quantitative measurement of electromagnetic
energy and its application to imaging sensors
III.2D1
III.2D2
III.2D3
III.2D4
Define Radiometric and photometric quantities (SI
units)
Describe optical properties of materials and
components
Explain principles, approaches and tools of radiometric
calibration/normalization
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3. Knowledge Unit title: Electro-optical (EO) Sensor Science
A. Knowledge Unit description and objective: Understand and be familiar with passive
visible and infrared phenomenology, theory, EO sensor design and their applications.
B. Requirement satisfaction: This KU is satisfied when all Topics and all Learning Objectives
are met.
Electro-optical (EO) Sensor Science
COURSE/CLASS
ID
TOPICS
Types of electro-optical sensors
III.3C1
III.3C2
Ultraviolet, visible and shortwave-midwave-longwave
infrared spectral measurement theories, principles,
techniques and their applications
Reflected and emitted energy spectral signatures
III.3C3
III.3C4
EO data corrections (atmospheric interactions,
windows and absorption regions/bands)
Theories, principles, types and designs of EO sensors
III.3C5
EO sensor applications
III.3C6
LEARNING OBJECTIVES
Describe types of EO sensors (passive/active collector,
staring/scanning array, single/multiple waveband)
III.3D1
III.3D2
III.3D3
Explain atmospheric effects and corrections for EO
sensors
Describe reflected and emitted energy and spectral
signature generation.
Describe EO sensor applications based on sensor type
III.3D4
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4. Knowledge Unit title: Thermal Remote Sensing
A. Knowledge Unit description: Skills and basic knowledge required to comprehend
concepts, issues and applications relating to thermal imaging systems
B. Requirement satisfaction: This KU is satisfied when at least seven (7) Topics and all
Learning Objectives are met.
Thermal Remote Sensing
COURSE/CLASS
ID
III.4C2
TOPICS
Principles of thermal remote sensing (Planck’s
Function, Stefan-Boltzman Law
Atmospheric effects on thermal remote sensing
III.4C3
Spectral emissivity and kinetic temperature
III.4C4
Factors affecting kinetic temperature
III.4C5
Radiant temperature
III.4C1
III.4C7
Thermal remote sensing data acquisition modes
(active/passive, single/multiple waveband, day/night
collection) and thermal sensor platforms
Spatial resolution and geometric corrections
III.4C8
Thermal remote sensing applications
III.4C9
III.4C10
Measured radiance as a function of observed
material temperature and emissivity
Methods to separate temperature and emissivity
Thermal hyperspectral systems
III.4C6
III.4C11
III.4D1
III.4D2
LEARNING OBJECTIVES
Describe the principles of thermal remote sensed
imaging, platforms, sensor types, its limitations and
data processing methodologies
Describe thermal remote sensed data applications
5. Knowledge Unit title: Radar Remote Sensing
A. Knowledge Unit description and objective: Understand and comprehend radar remote
sensing, theory, design and applications.
B. Requirement satisfaction: This KU is satisfied when all Topics and all Learning Objectives
are met.
Basic Radar Science
COURSE/CLASS
ID
TOPICS
Microwave remote sensor system types
III.5C1
III.5C2
Microwave frequency measurement theory,
techniques and design
Applications of radar remote sensing
III.5C3
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Basic Radar Science
COURSE/CLASS
ID
LEARNING OBJECTIVES
Define the different types of microwave remote
sensor systems
III.5D1
III.5D2
Describe microwave signal generation, propagation,
target interaction, receipt, recording and
measurement
Discuss atmospheric effects on radar operation
III.5D3
Describe applications of microwave remote sensing
III.5D4
6. Knowledge Unit title: Lidar Remote Sensing
A. Knowledge Unit description and objective: Comprehend skills and knowledge of Lidar
remote sensing, how Lidar data is collected, processed and its applications.
B. Requirement satisfaction: This KU is satisfied when at least all Topics and all Learning
Objectives are met.
Lidar Remote Sensing
COURSE/CLASS
ID
TOPICS
Fundamentals of Lidar remote sensing
III.6C1
Lidar sensors
III.6C2
Lidar data formats
III.6C3
Lidar analysis tools and analytical methodologies
III.6C4
Lidar point classification
III.6C5
Lidar remote sensing applications
III.6C6
LEARNING OBJECTIVES
Describe the fundamentals of Lidar remote sensing
III.6D1
List and define Lidar data types
III.6D2
III.6D3
Describe Lidar analysis tools and analytical
methodologies
Explain Lidar point classification
III.6D4
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7. Knowledge Unit title: Remote Sensing Data Analysis
A. Knowledge Unit description and objective: Introduction to the basic applications of
quantitative remote sensing data analysis and the mathematical tools used for data exploitation
B. Requirement satisfaction: This KU is satisfied when all Topics and all Learning Objectives
are met.
Remote Sensing Data Analysis
COURSE/CLASS
ID
III.7C1
TOPICS
Mathematical frameworks for algorithm
development (multivariate statistics, linear algebra
and subspace geometry, spectral linear mixture
model, basic signal detection theory)
Spectral Classification Algorithms (supervised and
unsupervised, minimum distance to the mean,
Mahalanobis distance, Gaussian maximum
likelihood)
III.7C2
Spectral signature analysis algorithms (band ratio
analysis such as NDVI, NDWI), geologic mineral
analysis
III.7C3
III.7C4
Spectral Detection algorithms (anomaly detection
such as RX, change detection such as chronocrome,
covariance equalization), target detection (such as
GLRT, spectral matched filter, ACE, CEM)
Linear spectral un-mixing
III.7C5
LEARNING OBJECTIVES
Explain the (semi-) automated applications of
quantitative remote sensing image analysis
III.7D1
Describe the mathematical principles behind
quantitative remote sensing image analysis
III.7D2
Identify the limitations of quantitative remote
sensing image analysis
III.7D3
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8. Knowledge Unit title: Digital Image Processing
A. Knowledge Unit description and objective: Be familiar with and understand digital
processing of remote sensing imagery and data.
B. Requirement satisfaction: This KU is satisfied when at least five (5) Topics and at least
four (4) Learning Objectives are met.
Digital Image Processing
COURSE/CLASS
ID
TOPICS
Radiometric and geometric correction
III.8C1
III.8C2
Image enhancement, transformation, filtering,
resampling, mosaicking, interpolation and
restoration.
Image classification
III.8C3
III.8C4
III.8C5
III.8C6
Raster/data conversion, compression, storage
format and representation
Image processing algorithms and techniques to
support image enhancement; image filtering,
resampling, interpolation
Automatic and assisted feature recognition
algorithms and their limitations
Point and feature matching algorithms
III.8C7
III.8D1
III.8D2
III.8D3
III.8D4
III.8D5
LEARNING OBJECTIVES
Define processes to prepare raster imagery for
analysis
Define or demonstrate supervised and
unsupervised classification
Apply methods to classify an image into various
features and classes
Explain the concepts of digital counts, image
histogram processing, and compression
Demonstrate basic proficiency in the
computational manipulation of imagery
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9. Knowledge Unit title: Computational Radiometry
A. Knowledge Unit description and objective: Understand skills and knowledge required to
develop, generate, and apply synthetic scenes
B. Requirement satisfaction: This KU is satisfied when all Topics and all Learning Objectives
are met.
Computational Radiometry
COURSE/CLASS
ID
III.9C1
TOPICS
Imaging system modeling (scene/sensor/processing
parameters)
Understanding of material and optical properties
III.9C2
Understanding of atmospheric modeling
III.9C3
Scene construction basics and geometry modeling
III.9C4
III.9C5
Applications of computational radiometry
LEARNING OBJECTIVES
Discuss imaging system modeling
III.9D1
Perform, test and evaluate imaging modeling
III.9D2
III.9D3
Discus the applications of computational
radiometry
10. Knowledge Unit title: Imagery/Satellite Image Time Series Analysis
A. Knowledge Unit description: Skills and basic knowledge of temporal imagery/satellite
image time series analysis and its applications.
B. Requirement satisfaction: This KU is satisfied when all Topics and Learning Objectives are
met.
Imagery Time Series Analysis
COURSE/CLASS
ID
III.10C1
III.10C2
III.10C3
TOPICS
Imagery (ITS)/Satellite image time series (SITS)
analysis
Time series analytical tools, methodologies and
processes
ITS/SITS time scales
Image time series applications
III.10C4
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Imagery Time Series Analysis
COURSE/CLASS
ID
III.10D1
III.10D2
III.10D3
LEARNING OBJECTIVES
Explain ITS/SITS analytical tools, methodologies
and processes
Discuss the merits and differences between the
various ITS/SITS time scales
Discuss the various applications for ITS/SITS
analysis
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