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Unsupervised spatio-temporal data mining framework for burned area mapping

机译:烧区映射的无监督时空数据挖掘框架

摘要

A method reduces processing time required to identify locations burned by fire by receiving a feature value for each pixel in an image, each pixel representing a sub-area of a location. Pixels are then grouped based on similarities of the feature values to form candidate burn events. For each candidate burn event, a probability that the candidate burn event is a true burn event is determined based on at least one further feature value for each pixel in the candidate burn event. Candidate burn events that have a probability below a threshold are removed from further consideration as burn events to produce a set of remaining candidate burn events.
机译:一种方法通过接收图像中每个像素的特征值来减少识别被火烧毁的位置所需的处理时间,每个像素代表一个位置的子区域。然后根据特征值的相似性对像素进行分组,以形成候选烧录事件。对于每个候选燃烧事件,基于候选燃烧事件中的每个像素的至少一个另外的特征值,确定候选燃烧事件是真实燃烧事件的概率。将概率低于阈值的候选燃烧事件作为燃烧事件从进一步考虑中删除,以产生一组剩余的候选燃烧事件。

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