Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In p...

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Bibliographic Details
Main Authors: Yuguo Qian, Weiqi Zhou, Jingli Yan, Weifeng Li, Lijian Han
Format: Article
Language:English
Published: MDPI AG 2014-12-01
Series:Remote Sensing
Subjects:
object-based classification
machine learning classifiers
very high resolution image
urban area
tuning parameters
Online Access:http://www.mdpi.com/2072-4292/7/1/153

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