Learn Hyperspectral Remote Sensing from the Scratch

Learn Hyperspectral Remote Sensing from the Scratch

Understand primary concepts, methods and algorithms of imaging spectroscopy.

Hyperspectral Earth Observation

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You won’t regret it!

Understanding a problem or project that involves satellite imagery can be very difficult and easily can come to a dead-end. There are numerous of Earth Observation satellites orbiting the earth and produce vast amount of data. Data that need to be processed, analyzed, and valuable information to be extracted.

Earth Observation satellites or Remote Sensing satellites contain payloads (sensors) that capture parts or the entire globe at different wavelengths (spectral bands). A primary categorization of these sensors are:

  • Optical (multispectral, hyperspectral)
  • Thermal (multispectral, hyperspectral)
  • Synthetic Aperture RADAR (SAR)

Of course there are a few other types sensors, but the above are the most mature and used in an operational manner. The main focus of the course is at the hyperspectral optical remote sensing. Due to the nature of the subject, several concepts, processing chains, algorithms and methods discussed in this course are also applicable to other domains (optical multispectral and thermal).

Based on my past experience, research and knowledge I composed this course with one thing in mind: help students, professionals, or even researchers to understand the main concepts of hyperspectral imagery and how you can place them in your everyday-work-life. Starting from a quick introduction about remote sensing and hyperspectral imaging, we continue to the various applications hyperspectral data are being used (from the Earth Observation perspective). At the core of the course, students get familiar with the main processing concepts and techniques applied on hyperspectral data. Four major processing workflows are being analysed:

  1. Spectral Mixing and Unmixing
  2. Spectral Matching and Labeling
  3. Spectral Library Building and Updating
  4. Spectroscopy and Object Parameter Estimation 

In each of these series of lectures, enrolled students are provided with extensive written documentation to further study the presented concepts and methods.

This course is recommended for anyone who needs to understand and start working with hyperspectral data and imagery. People who are about to start either a Remote Sensing project or start to learn the basics of remote sensing, as well as those who have come to a dead-end in the middle of a remote sensing/earth observation project and need to know how hyperspectral data can help them overcome their problems.


What will you learn by enrolling?

Just see our introductory video!

What will you learn?

Understand the principles of hyperspectral remote sensing

Know at which Earth Observation applications hyperspectral data are used

Become familiar with hyperspectral data processing concepts

Learn about the spectral unmixing process can and what you can do with this

Understand spectral matching & labeling methods

Learn about spectroscopy and quantitative spectral analysis

Any prerequisites?

Practically none.

A basic understanding of remote sensing or GIS will only help you to finish the course faster

Student Profile?

This course is meant for newbies (students and/or professionals) who are not very familiar with workflows, methods, and data needed to approach and solve a Remote Sensing problem.

The current course is based on years of professional and research experience in hyperspectral algorithm design and implementation

This course is not a tutorial on any software or a guide for specific algorithms. The main concept of the course is to introduce the student with the aspects of Hyperspectral Remote Sensing. It is structured in a way the student doesn't need to have experience in remote sensing or Earth Observation.

Anyone who wants to understands the benefits of using hyperspectral imagery in real problems


Hyperspectral Remote Sensing Explained

  • Principles of Hyperspectral or Imaging Spectroscopy concepts FREE
  • Bonus Material: Principles of Hyperspectral or Imaging Spectroscopy concepts FREE
  • Overview of Information Extraction Methods Categories

Hyperspectral Imaging

  • Hyperspectral Images
  • Satellites with Hyperspectral Imaging Capabilities
  • Bonus Material: Satellites with Hyperspectral Imaging Capabilities

Spectral Mixture Analysis

  • Spectral Mixture Analysis
  • Bonus Material: Spectral Mixture Analysis

Spectral Unmixing Process

  • Spectral Unmixing: Step by step process
  • Bonus Material: Spectral Unmixing Process
  • Bonus Material: Dimensionality Estimation and Reduction
  • Bonus Material: Endmember Extraction Algorithms
  • Bonus Material: Abundance Estimation Algorithms

Spectral Matching and Labeling

  • Spectral Similarity Measures, Matching & Labeling
  • Bonus Material: Spectral Similarity Measures
  • Bonus Material: Spectral Pre-Processing Algorithms
  • Bonus Material: Spectral Pre-Processing Algorithms
  • Bonus Material: Spectral Matching and Labeling Process

About Your Instructor

Dimitris Sykas

Dimitris Sykas

Remote Sensing Expert

I'm a Remote Sensing and a Surveying Engineer. I received my degree from NTUA in 2010, where I also received my Ph.D. in hyperspectral remote sensing in 2016. From graduation in 2010, my career started as a Researcher Associate and Teaching Associate in the Laboratory of Remote Sensing of NTUA. From that time I also worked at several private companies as a Remote Sensing Expert and Geospatial Analyst. From the beginning of 2015 I was positioned as Senior Earth Observation Expert. During these years, I have participated in more than 20 funded European Commission and European Space Agency projects, have over 16 peer reviewed scientific publications in the field of Remote Sensing, and have an international patent in hyperspectral data compression.

My main research and professional interests are in the optical remote sensing area, where I specialize in data (images, point measurements) processing and algorithm design and development. Some of the software tools that I operate to accomplish my research and business dreams are ENVI, IDL, QGIS, ERDAS Imagine, ArcGIS, and Python. I have been working with these tools since 2008.



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