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What is High-Res Urban Remote Sensing Classification about?

This article presents a novel automated framework for classifying high-resolution remote sensing data using a Random Forest ensemble and Fully Connected Conditional Random Field. The method involves three stages: feature extraction from multispectral images and 3D geometry data, classification using an ensemble of Random Forest classifiers, and refinement of results through a Conditional Random Field model. Experiments demonstrate that this approach achieves an overall accuracy of 86.9%, marking it as a sta

Author

mramos.mins

Language

EN