Hyperspectral & Multispectral Imaging Training
Hyperspectral & Multispectral Imaging Training Course Description
This three-day Hyperspectral & Multispectral Imaging Training 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. Case studies of applications will be used throughout the course. After taking this course, students should be able to communicate and work productively with other professionals in this field.
• The properties of remote sensing systems
• How to match sensors to project applications
• The limitations of passive optical remote sensing systems and the alternative systems that address these limitations
• The types of processing used for classification of image data
• Evaluation methods for spatial, spectral, temporal and radiometric resolution analysis
With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.
Introduction to Multispectral and Hyperspectral Remote Sensing
Sensor types and characterization. Design trade-off applications
Optical properties for remote sensing.
Sensor modeling and evaluation. Spatial, spectral, and radiometric
Multivariate Data Analysis. Scatterplots, impact of sensor performance on data characteristics
Assessment of unique signature characteristics. Differentiation of water, vegetation, soils and urban infrastructure.
LIDAR systems and applications.
Hyperspectral Data Analysis. Frequency band selection and band combination assessment
Matching sensor characteristics to study objectives. Sensor matching to specific application examples
Classification of Remote Sensing Data. Supervised and unsupervised classification; Parametric and non-parametric classifiers
Application case studies. Application examples used to illustrate principles and show in-the-field experience
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