Classification using Orfeo Toolbox

Hyperspectral Image Classification with Orfeo Toolbox

Images often speak louder than a thousand words, especially when it comes to geodata. With hyperspectral image classification, you can extract information from satellite images that isn’t visible to the naked eye. This technique uses hundreds of narrow spectral bands to identify what something on Earth is made of—whether it’s crops, soil, water, or buildings.

One tool that excels in this area is the Orfeo Toolbox (OTB). This is a free, powerful, and lightning-fast open-source software library that allows you to analyze and classify hyperspectral images. The code is highly optimized, allowing you to process large amounts of data efficiently. Thanks to its short learning curve, OTB is easily accessible to beginners, while its API and executable structure also make it appealing to experienced users. The toolbox contains various algorithms to filter, segment, and convert data into actionable insights—with applications in agriculture, environmental analysis, and urban planning, among others.

In this course, you’ll learn how to apply this technology in practice, step by step. You don’t need to be a programming expert. With clear explanations, personalized guidance, and realistic datasets, you’ll perform analyses yourself—and discover how to apply these insights within your field of geoinformation.

What will you learn in this Blended Learning course?

In this course, you’ll learn how to work independently on hyperspectral image classification using Orfeo Toolbox. You’ll start with the basics: what exactly hyperspectral data is, how it differs from regular image data, and why it’s so valuable when analyzing geodata.

Then you’ll get to work with various classification techniques. Methods such as supervised learning (where labels are assigned in advance) and unsupervised learning (where the software recognizes patterns on its own) are covered in detail. You’ll work with algorithms such as Support Vector Machines (SVM) and K-means clustering—techniques commonly used in applications like agricultural monitoring, environmental analysis, and urban development.

Recognizing spectral signatures is also part of the training. You’ll discover how different materials reflect light in unique ways, and how to use that information to distinguish objects in satellite images. During the practical assignments, you’ll work with raw images, learn how to prepare data, and perform complete classifications, step by step.

By the end of the course, you will have the skills to independently perform spatial analyses using open-source tools such as Orfeo Toolbox—and to actually apply those insights in practice.

Why choose this Classification with Orfeo Toolbox course?

Blended learning combines independent online learning with hands-on, interactive sessions. This way, you gain both theoretical knowledge and practical experience in hyperspectral image classification with Orfeo Toolbox. The online modules give you the freedom to learn at your own pace. You’ll follow clear, step-by-step lessons on image analysis, classification methods, and working with geospatial data. You’ll discover how to prepare, process, and classify hyperspectral images with OTB and how to apply these insights in real-world scenarios.

During the hands-on sessions, you’ll immediately put your knowledge into practice. You’ll work with real satellite images and geodata, and receive guidance from experts in remote sensing and geoinformation. You’ll learn how to interpret spectral data, verify classifications, and convert results into clear maps. This way, you’ll gain a firm grasp of the entire process: from raw data to usable analysis.

Thanks to this combination of flexible online learning and practical assignments, you will not only learn to work with the Orfeo Toolbox but also understand how to use it effectively for real-world image classification projects. Upon completion, you will be able to independently analyze hyperspectral images and use the results to inform spatial decisions within your field.