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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >A Cloud Detection Method Based on Relationship Between Objects of Cloud and Cloud-Shadow for Chinese Moderate to High Resolution Satellite Imagery
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A Cloud Detection Method Based on Relationship Between Objects of Cloud and Cloud-Shadow for Chinese Moderate to High Resolution Satellite Imagery

机译：基于云对象与云影关系的中国中高分辨率卫星影像云检测方法

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摘要

Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors do not have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called object-oriented cloud and cloud-shadow matching method (OCM) is presented in this paper. It first modified the automatic cloud cover assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold settings produce different cloud maps. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Second, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Third, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. The OCM method was tested using almost 200 HJ-1/CCD and GF-1/WFV images across China and the overall accuracy of cloud detection is close to 90%.
机译：卫星图像的云检测对于定量遥感研究和遥感应用非常重要。但是，许多卫星传感器没有足够的频带来快速，准确和简单地检测云。特别是中国新推出的中高空间分辨率卫星传感器，例如中国“环晶1号”（HJ-1 / CCD）机载电荷耦合器件和机载宽视场（WFV）传感器等。高分1号（GF-1）仅具有四个可用频带，包括蓝色，绿色，红色和近红外频带，这与大多数可能的检测方法的要求相去甚远。为了解决这个问题，本文提出了一种改进的，自动的中国卫星传感器云检测方法，即面向对象的云和云影匹配方法（OCM）。它首先修改了针对Landsat-7数据开发的自动云覆盖评估（ACCA）方法，以获得初始云图。改进的ACCA方法主要基于阈值，并且不同的阈值设置会产生不同的云图。随后，使用严格的阈值来生成具有高置信度和大量云遗漏的云图，而使用宽松的阈值来生成具有低置信度和大量佣金的云图。其次，还使用近红外波段的阈值生成相应的云影图。第三，将云图和云影图转移到云对象和云影对象。云和云阴影通常成对出现。因此，最终的云图和云影图是基于云图与云影对象之间的关系制作的。在全国范围内使用近200幅HJ-1 / CCD和GF-1 / WFV图像对OCM方法进行了测试，云检测的整体准确性接近90％。

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