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Practical method of shadow detection and removal for high spatial resolution remote sensing image

机译:高空间分辨率遥感影像阴影检测与去除的实用方法

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摘要

High spatial resolution remote sensing image (HSRRSI) has received a warm welcome in many fields. However, building shadows of large area on HSRRSI (up to 30% in some cases) are one of the biggest hindrances for further applications in many fields. To keep a balance between precision and efficiency required by applications during shadow removal, this paper introduces a creative and practical strategy based on the theory of the pulse coupled neural network (PCNN). By applying the simplified model of PCNN, shadows on HSRRSI had been detected and removed respectively. When applied to HSRRSI, the method could not only remove the shadows, but also keep the contrast between removed areas with shadows and other areas without shadows from being too big, which might distort the image. Therefore the satisfactory result is gained.
机译:高空间分辨率遥感影像(HSRRSI)在许多领域都受到热烈欢迎。但是,HSRRSI上大面积的阴影(在某些情况下高达30%)是在许多领域中进一步应用的最大障碍之一。为了在去除阴影期间的应用程序所需的精度和效率之间保持平衡,本文基于脉冲耦合神经网络(PCNN)的理论介绍了一种新颖实用的策略。通过应用PCNN的简化模型,分别检测并消除了HSRRSI上的阴影。当应用于HSRRSI时,该方法不仅可以去除阴影,而且还可以使去除阴影的区域与没有阴影的其他区域之间的对比度过大,这可能会使图像失真。因此获得令人满意的结果。

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