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A class-wise spatial-contextual approach based on a free discontinuity model for change detection in multispectral images

机译:基于自由间断模型的分类空间上下文方法用于多光谱图像中的变化检测

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The increased radiometric resolution of last generation multispectral sensors results in large statistical variability of classes represented in the image. However, classes present high spatial homogeneity. To preserve classes identity while simplifying their representation, in this paper we propose a class-wise spatial-contextual method based on a variational model with free discontinuities that reduces the statistical variability of classes by emphasizing their spatial contours. To prove its effectiveness, the proposed method is applied in the context of change detection in multispectral images. Here, it is able to augment the discrimination between the unchange and the change classes and to improve the detection performance.
机译:上一代多光谱传感器的辐射分辨率提高,导致图像中表示的类别的统计差异很大。但是,类具有很高的空间同质性。为了在简化类表示的同时保留类的同一性,本文提出了一种基于具有自由间断的变分模型的类空间上下文方法,该方法通过强调类的空间轮廓来降低类的统计变异性。为了证明其有效性,将所提出的方法应用于多光谱图像中的变化检测。在此,能够增加不变性和变动类别之间的区别,并且能够提高检测性能。

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