Comparer des méthodes

	Classification d'images basée sur les pixels	Forêt Aléatoire
Domaine	Télédétection	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2007	2001
Auteur d'origine	Remote-sensing classification literature	Breiman, L.
Type	Supervised/unsupervised spectral image classification	Ensemble (bagging of decision trees)
Source fondatrice	Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	2	4
Résumé	Pixel-based image classification is a fundamental remote-sensing technique that assigns each individual pixel in a satellite or aerial image to a thematic land-cover category based solely on its spectral values across multiple bands. Systematically surveyed and formalized by Lu and Weng (2007), the approach encompasses both supervised methods—where labeled training samples guide the classifier—and unsupervised clustering approaches that discover natural spectral groupings without prior labels.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.