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Classification of satellite images is a key component of many remote sensingapplications. One of the most important products of a raw satellite image isthe classified map which labels the image pixels into meaningful classes.Though several parametric and non-parametric classifiers have been developedthus far, accurate labeling of the pixels still remains a challenge. In thispaper, we propose a new reliable multiclass-classifier for identifying classlabels of a satellite image in remote sensing applications. The proposedmulticlass-classifier is a generalization of a binary classifier based on theflexible ensemble of regression trees model called Bayesian Additive RegressionTrees (BART). We used three small areas from the LANDSAT 5 TM image, acquiredon August 15, 2009 (path/row: 08/29, L1T product, UTM map projection) overKings County, Nova Scotia, Canada to classify the land-use. Several predictionaccuracy and uncertainty measures have been used to compare the reliability ofthe proposed classifier with the state-of-the-art classifiers in remotesensing.

A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images