Working with Time Series Image Data as Frames in eCognition
Since it’s infancy, nearly 20 years ago, eCognition has included functionality to work with image data that represent a set of time frames. The t- dimension sits alongside the x-, y- and z- dimensions, representing the full 4D eCognition image data model. The t- dimension can be a powerful tool for classic change detection/analysis work, such as with multi-temporal satellite and aerial image data, but also for use with image data from time-lapse sensor and video systems that are increasingly used in field studies and experimental setups.
The aim of this webinar is to explore how to work in eCognition with the t- dimension, in terms of both entering image data as a frame series and using frame related features in rule sets, including the powerful Link Class (algorithm Create Links) functionality.
The use case presented in the webinar by Dr. Geoff Groom, a senior researcher at the Institute of Bioscience of Aarhus University in Denmark, looks at a set of multi-temporal tripod-mounted camera photos of a vegetated (Dryas octopetala) field plot in Greenland, collected as part of climate change ecology effect research.
Topics covered include:
- Setting up t- dimension projects
- Working with the t- dimension in rule sets
- t- dimension-based change detection strategies
Was this article helpful?
Articles in this section
- Data Fusion Approaches to Tree Canopy Change Detection
- Welcome to eCognition 10
- Timber Cruising with eCognition
- eCognition Developer for Beginners
- What’s New in eCognition 9.5
- Go Big with eCognition Server
- Agricultural Field Boundary Delineation with Multi Temporal Sentinel 2 Imagery
- Deep Learning, UAVs and Precision Agriculture
- Counting Features with Template Matching
- The Future of Vegetation Analyses in Feeder Corridors with YellowScan & Trimble