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- W4297792972 abstract "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." @default.
- W4297792972 created "2022-10-01" @default.
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- W4297792972 date "2013-04-15" @default.
- W4297792972 modified "2023-09-25" @default.
- W4297792972 title "A new Bayesian ensemble of trees classifier for identifying multi-class labels in satellite images" @default.
- W4297792972 doi "https://doi.org/10.48550/arxiv.1304.4077" @default.
- W4297792972 hasPublicationYear "2013" @default.
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