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Wetland Mapping Using Remote Sensing Imagery and ModelMap

Remote Sensing of Wetlands. Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB). Classific

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Wetland Mapping Using Remote Sensing Imagery and ModelMap

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    1. Wetland Mapping Using Remote Sensing Imagery and ModelMap Xuan Zhu Monash University, Australia

    2. Remote Sensing of Wetlands Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB) Large-area satellite remote sensing of wetlands is based on computer-based classification of multispectral image data. Classification techniques generally fall into one of two camps, supervised or unsupervised. Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods with the reported overall accuracy of over 70% (Sader et al. 1995; Baker et al., 2006). Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB). Large-area satellite remote sensing of wetlands is based on computer-based classification of multispectral image data. Classification techniques generally fall into one of two camps, supervised or unsupervised. Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods with the reported overall accuracy of over 70% (Sader et al. 1995; Baker et al., 2006). Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB).

    3. Classification Tree Analysis To determine a set of if-then logical (split) conditions that permit accurate prediction or classification of cases via tree-building algorithms In image classification, recursively parsing the training observations in a form of binary partitioning based on the values of the selected explanatory variables such as spectral responses and ancillary data

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