A new Bayesian ensemble of trees approach for land cover classification of satellite imagery

Classification of satellite images is a key component of many remote sensing applications. One of the most important products of a raw satellite image is the classification that labels image pixels into meaningful classes. Though several parametric and nonparametric classifiers have been developed thus far, accurate classification still remains a challenge. In this paper, we propose a new reliable multiclass classifier for identifying class labels of a satellite image in remote sensing applications. The proposed multiclass classifier is a generalization of a binary classifier based on the flexible ensemble of regression trees model called Bayesian Additive Regression Trees. We used three small areas from the LANDSAT 5 TM image, acquired on 15 August 2009 (path-row: 08-29, L1T product, UTM map projection) over Kings County, Nova Scotia, Canada, to classify the land cover. Several prediction accuracy and uncertainty measures have been used to compare the reliability of the proposed classifier with the state-of-the-art classifiers in remote sensing.