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ATI's Hyperspectral and Multispectral Imaging course
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Summary:
This three-day class is designed for engineers,
scientists and other remote sensing professionals
who wish to become familiar with multispectral
and hyperspectral remote sensing technology.
Students in this course will learn the basic
physics of spectroscopy, the types of spectral
sensors currently used by government and
industry, and the types of data processing used
for various applications. Lectures will be
enhanced by computer demonstrations. After
taking this course, students should be able to
communicate and work productively with other
professionals in this field. Each student will receive a complete set of notes and the textbook, Remote Sensing: The Image Chain Approach.
View course sampler
Read Dr. Gomez's techincal white papers
Instructor:
What You Will Learn:
- The limitations on passive optical remote sensing.
- The properties of current sensors.
- Component modeling for sensor performance.
- How to calibrate remote sensors.
- The types of data processing used for applications such as spectral angle
mapping, multisensor fusion, and pixel mixture analysis.
- How to evaluate the performance of different hyperspectral systems.
Course Outline:
- Introduction to multispectral and hyperspectral remote
sensing.
- Sensor types and characterization. Design tradeoffs.
Data formats and systems.
- Optical properties for remote sensing. Solar radiation.
Atmospheric transmittance, absorption and scattering.
- Sensor modeling and evaluation. Spatial, spectral, and
radiometric resolution.
- Statistics for multivariate data analysis. Scatterplots.
Impact of sensor performance on data characteristics.
- Spectral data processing. Data visualization and
interpretation.
- Radiometric calibration. Partial calibration. Relative
normalization.
- Image registration. Resampling and its effect on spectral
analysis.
- Data and sensor fusion. Spatial versus spectral algorithms.
- Classification of remote sensing data. Supervised and
unsupervised classification. Parametric and nonparametric
classifiers. Application examples.
- Hyperspectral data analysis.
Tuition:
Tuition for this three-day course is $1690 per person at one of our scheduled public courses. Onsite pricing is available. Please call us at 410-956-8805 or send an email to ati@ATIcourses.com.
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Dr. Richard Gomez is a Research Professor at
George Mason University (GMU) and Principal
Research Scientist at the Center for Earth
Observing and Space Research (CEOSR). At
GMU he teaches and is actively involved in the
scientific and technology fields of hyperspectral
imaging and high resolution remote sensing. He
has also served in industry and government (Texas
Instruments and USACE). Dr. Gomez is
internationally recognized as a leader and expert in
the field of spectral remote sensing (multispectral,
hyperspectral and ultraspectral) and has published
extensively in scientific journals. He has organized
and chaired national and international conferences,
symposia and workshops. He earned his doctoral
degree in physics from New Mexico State
University. He also holds an M.S. and a B.S. in
physics. Dr. Gomez currently serves as Director for
the ASPRS Potomac Region and as Remote
Sensing Chair for the IEEE-USA Committee on
Transportation and Aerospace Technology Policy.