NASA ARSET: Operational Crop Classification Roadmap using Optical and SAR Imagery 2, Part 4/5

Agricultural Crop Classification with Synthetic Aperture Radar and Optical Remote Sensing
Agricultural Crop Classification with Synthetic Aperture Radar and Optical Remote Sensing: Intermediate Webinar

Part 4: Operational Crop Classification Roadmap using Optical and SAR Imagery 2

Instructors: Georgia Karadimou & Tereza Roth (RUS)

- Explanation of Random Forest, Support Vector Machine, and Unsupervised algorithms as classifiers
- Explain Python libraries for running classifiers in JupyterLab
- Use of SNAP and Python for crop classification, including demonstration of different parameters (e.g., S1 vs. S2 alone) when classifying crop types in a given study area
- Q&A

If you would like to follow along with the demonstrations in Part 4, please:

Request the Training kit containing the Jupyter Notebook, environment setup, install instructions and training data by sending an e-mail to: eotraining@serco.com (Please specify that you are requesting the Training kit and include the name of the webinar series.