10. GMM Unsupervised Landcover Classification in Python | Remote Sensing & GIS скачать в хорошем качестве

10. GMM Unsupervised Landcover Classification in Python | Remote Sensing & GIS

In this tutorial of the Beginner Remote Sensing with Python series, we’ll classify land cover using Gaussian Mixture Models (GMM) on Landsat 8. No labels, just pure machine learning! GMM is a more flexible clustering method than K-means — it models clusters as elliptical Gaussians, provides soft probability boundaries, and often works better for complex land cover like turbid water, vegetation edges, and mixed soils. What you’ll learn in this video: How to prepare Landsat-8 bands for unsupervised classification How to build a feature matrix and standardize inputs How to apply Gaussian Mixture Models (GMM) for land cover mapping How to compare GMM results with a True Color composite (RGB B4-B3-B2) Why GMM can be more powerful than K-means for irregular land cover classes To learn more about Gaussian Mixture Models in Scikit-learn: https://scikit-learn.org/stable/modul... Previous tutorials in this Beginner Python series: Tutorial    • 6. Binary Landcover Classification (with A...   Tutorial    • 7. Multiclass Landcover Classification in ...   Tutorial    • 8. How to Extract Water with NDWI in Pytho...   Tutorial    • 9. K-Means Landcover Classification in Pyt...   This concludes the Beginner Python Series. Next, I’ll start beginner tutorials for QGIS, then return to Python for advanced remote sensing with supervised ML and deep learning. TIP: Try n_classes=5, 6, or 7 and compare the results. If this tutorial helped, please LIKE, SHARE, and SUBSCRIBE so you don’t miss the next series! #remotesensing #landsat #gaussianmixturemodel #GMM #landcoverclassification #machinelearning #classification #unsupervisedclassification #landcovermapping #geospatial #satelliteimagery #environmentalmonitoring #gis #coastalmanagement #coastalmapping