পদ্ধতির তুলনা করুন

	অবজেক্ট-ভিত্তিক চিত্র বিশ্লেষণ (OBIA)	Random Forest
ক্ষেত্র	দূর অনুধাবন	যন্ত্র শিখন
পরিবার	Process / pipeline	Machine learning
উদ্ভবের বছর	2010	2001
প্রবর্তক	Thomas Blaschke	Breiman, L.
ধরন	Image segmentation and classification pipeline	Ensemble (bagging of decision trees)
মৌলিক উৎস	Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
অপর নাম	Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
সম্পর্কিত	3	4
সারসংক্ষেপ	Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery.	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.