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Cloud Detection in MODIS data based on spectrum analysis and snake model

机译:基于频谱分析和蛇形模型的MODIS数据云检测

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Clouds in satellite images must be precisely identified prior to any further analysis in any case. A new cloud detection algorithm based on spectrum analysis and snake model is put forward in this paper. According to the distinction of the spectral bands for the MODIS and the spectrum curve of different objects, we can differentiate the cloud from other objects well by the band 1, band6 and band26. Because the distinct difference between cloud and the earth's surface, the detected thresholds have good robustness, and they are not sensitivity to images. Then we can optimize the cloud boundary by snake model. We adequately use the image information of the three bands in snake model via color gradient. By balancing these model-based and data-driven energy terms using regularization parameters, the snake algorithm can extract very accurate cloud boundaries without gaps and spurious branches. According to numerous experimental results, the new cloud detection algorithm in this paper is simple, feasible and suitable.
机译:在任何情况下,在进行任何进一步分析之前,都必须精确识别出卫星图像中的云。提出了一种基于谱分析和蛇形模型的云检测新算法。根据MODIS的光谱带和不同对象的光谱曲线的区别,我们可以通过频带1,band6和band26很好地将云与其他对象区分开。由于云与地球表面之间存在明显差异,因此检测到的阈值具有良好的鲁棒性,并且对图像不敏感。然后我们可以通过蛇模型优化云边界。我们通过颜色梯度充分利用了蛇模型中三个波段的图像信息。通过使用正则化参数平衡这些基于模型的能量和数据驱动的能量项,snake算法可以提取非常准确的云边界,而不会出现间隙和虚假分支。根据大量的实验结果,本文提出的新的云检测算法简单,可行,适用。

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