In this tutorial, we dive into unsupervised landcover classification using K-Means in Python — no labels, just pure machine learning!
Perfect for remote sensing and GIS beginners.
What You'll Learn:
Load and stack satellite image bands
Create a feature matrix (N×B format)
Standardize reflectance values
Run K-Means clustering to group pixels
Visualize a landcover classification map
Tips to choose the best k value
Previous Tutorials:
Tutorial mentioned in the video: https://www.youtube.com/watch?v=rKXqFeY53YU
Tutorial https://www.youtube.com/watch?v=IsJGISkTfGo
Tutorial https://www.youtube.com/watch?v=xQqC2CPszkE
Tutorial https://www.youtube.com/watch?v=gC1KUH1OJ-I
Learn More:
Scikit-learn K-Means: https://scikit-learn.org/stable/modules/clustering.html#k-means
TIP: The most important hyperparameter in K-means is k. Try k=5, k=6, or k=7 and compare the results.
If this helped, LIKE, SUBSCRIBE, and SHARE to grow with us.
#kmeans #remotesensing #gis #pythontutorial #landcoverclassification #machinelearning #geospatial #unsupervisedlearning #satelliteimagery
