A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images
This work addresses the problem of land-use classification in remote sensing for applications like environmental monitoring, though it appears incremental as it generalizes an existing binary classifier.
The authors tackled the challenge of accurately labeling pixels in satellite images by proposing a new Bayesian ensemble of trees classifier for multi-class classification, achieving competitive prediction accuracy and uncertainty measures compared to state-of-the-art methods on LANDSAT 5 TM data.
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 classified map which labels the image pixels into meaningful classes. Though several parametric and non-parametric classifiers have been developed thus far, accurate labeling of the pixels 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 (BART). We used three small areas from the LANDSAT 5 TM image, acquired on August 15, 2009 (path/row: 08/29, L1T product, UTM map projection) over Kings County, Nova Scotia, Canada to classify the land-use. 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.