Lab 5b: Improving Land Cover Classification Using Optical and SAR Imagery in Python скачать в хорошем качестве

Lab 5b: Improving Land Cover Classification Using Optical and SAR Imagery in Python

Summary In this tutorial, we build on Lab 5a by addressing land cover misclassifications such as confusion between built-up areas and bare areas. We will combine optical (Sentinel-2) and SAR (ALOS PALSAR-2) imagery. What you will learn: ✅ How to enhance classification accuracy by combining spectral and radar data ✅ Understanding ALOS PALSAR-2 ScanSAR HV polarization and its benefits for land cover mapping ✅ Loading and combining Sentinel-2 and PALSAR-2 data in Python ✅ Defining features and training a Random Forest classifier ✅ Saving the trained model for future predictions We focus on using the ALOS PALSAR-2 HV polarization to complement the Sentinel-2 bands, creating a more robust feature set for classification. By the end of this lab, you will have a trained model that integrates SAR and optical data. Additional Materials: 1. Python Script https://github.com/ck1972/Geospatial-... 2. Access courses at Ai. Geelabs https://aigeolabs.com/courses/ https://aigeolabs.com/sign-up/ 3. Buy 'Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach' book https://aigeolabs.com/books/explainab...