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Dehazing Method for Hyperspectral Remote Sensing Imagery with Hyperspectral Linear Unmixing

机译：高光谱线性分解的高光谱遥感影像去雾方法

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摘要

Haze always exists in hyperspectral remote sensing imagery, and it is a key reason that influences the effective information extraction of hyperspectral images. Specially, when the faint haze covers part of the target in remote sensing images, the target still can be detected but not clear. So, how to remove the influence of the haze and improve the applicable efficiency of hyperspectral images is a popular research point. This paper proposes a dehazing method for hyperspectral images based on linear unmixing. First, a popular hyperspectral unmixing method called FUN is used to get the signature of all the endmembers and their corresponding abundance. And then, the abundance of the haze endmember is removed and the abundances of the rest endmembers are adjusted to satisfy the sum-to-one and non-negative constraint. Lastly, the new abundance and the signature of the endmembers are linearly mixed to get the dehazed hyperspectral images. The experiment result shows that the dehazed hyperspectral images exhibit better target information and details. The method is effective and available.
机译：雾度始终存在于高光谱遥感影像中，这是影响高光谱影像有效信息提取的关键原因。特别是，当微雾笼罩遥感图像中的部分目标时，仍然可以检测到目标但不清楚。因此，如何消除雾度的影响并提高高光谱图像的适用效率是当前的研究热点。提出了一种基于线性分解的高光谱图像去雾方法。首先，一种流行的高光谱分解方法称为FUN，用于获取所有末端成员的签名及其相应的丰度。然后，去除雾度末端成员的丰度，并调整其余末端成员的丰度，以满足合计与非负约束。最后，将新的丰度和末端成员的特征进行线性混合，以获得去雾的高光谱图像。实验结果表明，经去雾处理后的高光谱图像具有更好的目标信息和细节。该方法是有效且可用的。

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