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

Описание: 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: [email protected] (Please specify that you are requesting the Training kit and include the name of the webinar series.

Exercise Steps: http://appliedsciences.nasa.gov/sites...

Download the Processing Data: https://drive.google.com/file/d/156ha...

You can access all training materials from this webinar series on the training webpage: https://appliedsciences.nasa.gov/join...

This training was created by NASA's Applied Remote Sensing Training Program (ARSET), in partnership with Agriculture and Agri-Food Canada (AAFC), European Space Agency (ESA), Indian Space Research Organisation (ISRO), United Nations Office for Outer Space Affairs (UNOOSA), and the CEOS Working Group on Capacity Building & Data Democracy (WGCapD). ARSET is a part of NASA's Applied Science's Capacity Building Program. Learn more about ARSET: https://appliedsciences.nasa.gov/what...