Land Cover Mapping Using Combination and Ensemble Classifiers
نویسندگان
چکیده
In recent years, large scale land cover maps constructed from remotely sensed data have become important information sources for resource management. Classifiers are commonly used to predict land cover for unsampled map units; hence, they play a key role in map construction. Achieving adequate classifier accuracy is often problematic for highly variable and difficult-to-sample landscapes. This article investigates a variety of methods for improving accuracy based on 1) combining a few different classifiers, and 2) creating ensembles of many classifiers. In addition, we derive an analytic expression for the exact bagging -nearest neighbor classifier.
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