使用期限*
许可形式单机版
原产地美国
介质下载
适用平台Windows
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IDRISI has the broadest set of classification procedures in the industry including both hard and soft classification
procedures based on machine learning (such as neural networks) and statistical
characterization
Soft classifiers and mixture analysis tools include a Bayesian
probability classifier, a Mahalanobis distance classifier, fuzzy set classifier, and linear
spectral unmixing.
Options
exist to incorporate planning interventions such as incentives and constraints,
proposed reserve areas and infrastructural changes.
DECISIONFOREST – an implementation of the random forest algorithm for classification. In addition to the hard classification, probabilistic soft classification images can also be output. DECISIONFOREST models can also be saved and used to classify novel imagery. Thus models can be trained in one location, or time, and then applied in a different location or time so long as the same variables (bands) are used.
CONSENSUS – a module that outputs the most common class assignment from a group of classifications. This is useful for the creation of ensemble classifications.
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