Change detection approach using evidential fusion of change indices

A. Bouakache, A. Tahraoui, R. Khedam, A. B. Aissa
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引用次数: 1

Abstract

In this paper, we present fusion and classification process of change indices using multitemporal satellites images in the aim to detect the change of surface states after a flood. This process is performed in the framework of Dempster Shafer Theory (DST), which takes into account the imprecision and the ignorance related to data. We apply this process to a study site located at south west of England, traversed by Severn river, which have undergone in October 2000 an important flood. For the detection of the flood damage, we have used two change indices: difference values and texture evolution. We find that change index fusion overcomes the limits of change mono-index classification.
基于变化指标证据融合的变化检测方法
本文提出了基于多时相卫星图像的变化指数融合与分类方法,以检测洪水后地表状态的变化。这个过程是在Dempster Shafer理论(DST)的框架下进行的,该理论考虑了与数据相关的不精确和无知。我们将这一过程应用于位于英格兰西南部的一个研究地点,该地点由塞文河穿过,在2000年10月经历了一次重要的洪水。对于洪涝灾害的检测,我们采用了差值和纹理演化两种变化指标。我们发现变化指标融合克服了变化单指标分类的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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