How can you uncover more from your geospatial data using Network Spectral Unmixing? In this one-on-one blended learning course, you’ll dive into the science of spectral unmixing and discover how to break down complex hyperspectral imagery for deeper, more accurate insights. With personal guidance and hands-on assignments, you’ll apply powerful techniques right away to sharpen your spatial analysis skills.
Network Spectral Unmixing (NSU) is a powerful technique that helps you extract detailed information from geospatial data. It works by separating spectral signals from mixed pixels to identify the individual materials or surfaces present—like vegetation, water, or urban areas.
By combining advanced analytics with network-based models, NSU enhances the clarity of spectral signals while reducing background noise. This results in cleaner, more reliable data—perfect for remote sensing, environmental monitoring, land use classification, and more.
Even when materials look similar on the surface, NSU allows you to detect subtle differences. That’s why it’s a valuable tool in applications ranging from ecosystem monitoring to urban planning.
This course offers a comprehensive introduction to Network Spectral Unmixing—beginning with the fundamentals and progressing to advanced workflows. Along the way, you’ll explore the Network-Based Method (NBM), a powerful approach that enhances the accuracy of spectral analysis.
Through hands-on practice, you’ll learn to:
In addition, you’ll evaluate your results using both synthetic and real-world datasets to understand how these techniques perform in practice.
By the end of the course, you’ll confidently interpret hyperspectral data and apply your knowledge to real geospatial analysis projects.
Blended learning gives you the best of both worlds—live interaction and flexible, self-paced study—so you can build job-ready skills on your own schedule.
We begin with a live session where you’ll get hands-on with real hyperspectral datasets. With expert support, you’ll dive into endmember identification, abundance estimation, and the practical use of NBM techniques.
Then, through our online modules, you’ll explore key topics at your own pace. You’ll study spectral signal breakdown, noise reduction, and method evaluation. Along the way, you’ll practice applying different unmixing models to improve your accuracy.
Later, in a second live session, you’ll apply what you’ve learned in a real-world scenario. You’ll refine your workflow, solve analysis challenges, and get one-on-one feedback from instructors.
A highlight of the course is its case-based format. You’ll create useful outputs—like classification maps and endmember reports—that you can apply directly in your research or job.
By combining expert-led training with practical tasks, this course goes beyond theory. You’ll finish with the skills and confidence to use NSU in your work and make smarter, data-driven decisions.
After completing this course, you’ll be able to:
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
