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ICA-based multi-temporal multi-spectral remote sensing imageschange detection

机译：基于ICA的多时相多光谱遥感影像变化检测

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摘要

Change detection is the process of identifying difference in the scenes of an object or a phenomenon, by observing the same geographic region at different times. Many algorithms have been applied to monitor various environmental changes. Examples of these algorithms are difference image, ratio image, classification comparison, and change vector analysis. In this paper, a change detection approach for multi-temporal multi-spectral remote sensing images, based on Independent Component Analysis (ICA), is proposed. The environmental changes can be detected in reduced second and higher-order dependencies in multi-temporal remote sensing images by ICA algorithm. This can remove the correlation among multi-temporal images without any prior knowledge about change areas. Different kinds of land cover changes are obtained in these independent source images. The experimental results in synthetic and real multi-temporal multi-spectral images show the effectiveness of this change detection approach.
机译：变化检测是通过在不同时间观察同一地理区域来识别对象或现象场景中差异的过程。已经应用了许多算法来监视各种环境变化。这些算法的示例是差异图像，比率图像，分类比较和变化向量分析。提出了一种基于独立分量分析（ICA）的多时相多光谱遥感图像变化检测方法。利用ICA算法，可以减少多时相遥感影像中二阶和更高阶相关性，从而检测环境变化。这可以消除多时间图像之间的相关性，而无需任何有关变化区域的先验知识。在这些独立的源图像中获得了不同类型的土地覆被变化。在合成和真实的多时间多光谱图像中的实验结果表明了这种变化检测方法的有效性。

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