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首页> 外文期刊>Quality Control, Transactions >Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered
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Cloud Extraction Scheme for Multi-Spectral Images Using Landsat-8 OLI Images With High Brightness Reflectivity Covered

机译：利用具有高亮度反射率的Landsat-8 OLI图像的多光谱图像云提取方案

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摘要

Cloud extraction is a vital step in remote sensing image processing. Although many advanced cloud extraction methods have been proposed and confirmed to be effective in recent years, there are still difficulties in cloud extraction in areas of high brightness reflectivity covered. High brightness reflectivity cover can have similar spectral characteristics as clouds, and thus, it is easily confused with clouds in cloud extraction schemes. This work presents a novel scheme designed to extract clouds in satellite imagery with high brightness reflectivity covered. The fractal summation method and spatial analysis are used to extract the clouds in the Landsat 8 Operational Land Imager (OLI) images containing high brightness reflectivity covered. The scheme consists of three main steps: cloud extraction based on pixel values, Anselin Local Moran's I value, and anisotropy. Pixel values were applied to extract the clouds associated with anomalies, and the last two steps were conducted to eliminate false anomalies. The findings showed that the cloud-associated anomaly pixel-values well approximate a power-law function, but both the real and fake anomaly patches (e.g., snow/ice, desert, etc.) routinely coexist within the same (fractal) scaleless segments, and that the latter seems to be more significant than the former. Consequently, these results indicate that the diagnostic difference between true and false anomalies must lie in their spatial distribution patterns. Furthermore, experiments confirmed that the fractal dimension and spatial distribution (i.e. Anselin Local Moran's I index and anisotropy) difference between the real and false anomalies displayed a certain universality. The proposed scheme effectively reduces the confusion and misclassification caused by cloud, snow and the highlighted underlying surface. It is of great significance for cloud restoration processing, image analysis, image matching, target detection and extraction, and effective extraction and utilization of remote sensing data.
机译：云提取是遥感图像处理的重要步骤。虽然近年来提出了许多先进的云提取方法并确认是有效的，但在覆盖高亮度反射率的区域云提取仍有困难。高亮度反射率覆盖可以具有与云相似的光谱特性，因此，云提取方案中的云很容易混淆。这项工作介绍了一种新颖的方案，旨在提取卫星图像中的云层，具有高亮度反射率。分形求和方法和空间分析用于提取覆盖高亮度反射率的Landsat 8运行陆地成像器（OLI）图像中的云。该方案由三个主要步骤组成：基于像素值，Anselin Local Moran的I值以及各向异性的云提取。应用像素值以提取与异常相关的云，并进行最后两个步骤以消除错误的异常。结果表明，云相关的异常像素值良好地近似幂律函数，而是真实和假的异常贴片（例如，雪/冰，沙漠等）常规共存在同一（分形）不可缩小的段内，后者似乎比前者更重要。因此，这些结果表明真实和假异常之间的诊断差异必须位于其空间分布模式中。此外，实验证实，分形维数和空间分布（即Anselin本地莫兰的I指数和各向异性）差异显示出一定的普遍性。拟议方案有效降低了云，雪和突出显示的下面引起的混乱和错误分类。对于云恢复处理，图像分析，图像匹配，目标检测和提取具有重要意义，有效提取和利用遥感数据。

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