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Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis
2004
Remote Sensing of Environment
Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of the
doi:10.1016/j.rse.2004.01.007
fatcat:qa2rwatc6zerzehm2fdvv5walu