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Salient region detection in remote sensing images based on color information content

机译:基于颜色信息内容的遥感图像显着区域检测

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Accurate and anti-noise detection of salient regions is a hotspot of remote sensing image analysis. In this paper, we introduce a new salient region detection model for residential areas in high-spatial-resolution remote sensing images, which is called Color Information Content model (CIC), applying color information content and outputting full resolution saliency maps. First, one-dimensional (1D) histograms of different color channels are constructed based on intensities. Second, the information content of intensities is computed on the 1D histograms and an information mapping is used to construct information maps which reflect information content of each colour channel. Finally, to establish saliency map, intensities of different color channels are fused by saliency scores based on information maps. Experimental results show that compared with existing models, our model not only gets accurate results effectively, but also has good noise immunity.
机译:显着区域的准确和抗噪检测是遥感图像分析的热点。在本文中,我们引入了一种新的针对高空间分辨率遥感影像中居住区的显着区域检测模型,称为颜色信息内容模型(CIC),该模型应用颜色信息内容并输出全分辨率显着性图。首先,基于强度构造不同颜色通道的一维(1D)直方图。其次,在1D直方图上计算强度的信息内容,并且使用信息映射来构造反映每个颜色通道的信息内容的信息映射。最后,为了建立显着性图,将不同颜色通道的强度与基于信息图的显着性分数进行融合。实验结果表明,与现有模型相比,该模型不仅可以有效地得到准确的结果,而且具有良好的抗噪能力。

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