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An explicit fuzzy supervised classification method for multispectral remote sensing images

机译:多光谱遥感图像的显式模糊监督分类方法

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Fuzzy classification has become of great interest because of its capacity to provide more useful information for geographic information systems. This paper describes an explicit fuzzy supervised classification method which consists of three steps. The explicit fuzzyfication is the first step where the pixel is transformed into a matrix of membership degrees representing the fuzzy inputs of the process. Then, in the second step, a MIN fuzzy reasoning rule followed by a rescaling operation are applied to deduce the fuzzy outputs, or in other words, the fuzzy classification of the pixel. Finally, a defuzzyfication step is carried out to produce a hard classification. The classification results on Landsat TM data demonstrate the promising performances of the method and comparatively short classification time.
机译:由于模糊分类能够为地理信息系统提供更多有用的信息,因此已引起人们极大的兴趣。本文描述了一种明确的模糊监督分类方法,该方法包括三个步骤。显式模糊化是第一步,其中将像素转换为表示该过程的模糊输入的隶属度矩阵。然后,在第二步中,应用MIN模糊推理规则,然后执行重新缩放操作,以推断出模糊输出,或者换句话说,得出像素的模糊分类。最后,执行去模糊化步骤以产生硬分类。 Landsat TM数据的分类结果表明该方法具有良好的性能,且分类时间相对较短。

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