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Received：01 August 2021，
Published：07 January 2023
移动端阅览
搭载在中国首颗海洋水色业务卫星HY-1C上的海岸带成像仪CZI（Coast Zone Imager）于2019年6月开始业务化运行，其获取的大量海岸带、陆地和海洋数据对于海洋灾害与环境监测研究具有重要意义。CZI对地观测受云干扰，影响数据的后续应用。现有的云检测算法大都基于RGB图像或者包含热红外的多光谱展开，对于HY-1C CZI这类RGB-近红外4个波段遥感图像研究较少。为此，本文提出了一种针对HY-1C CZI遥感图像的非监督遥感图像云检测方法。该方法包含训练样本选择、特征提取、支持向量机SVM（Support Vector Machine）分类和后处理这4个过程。在训练样本选择中，结合暗通道反射率、归一化植被指数和白度指数，提出了一种训练样本自动提取算法。该算法使用白度指数作为遥感图像细节信息提取数据源，并通过逐步细化过程精确提取样本。针对特征提取，选取图像的空—谱特征信息，包含反射率、光谱指数、纹理和结构特征，使云与非云区域的差异最大化。基于自动提取的训练样本及其特征描述，采用SVM对CZI遥感数据进行初分类，并在此基础上进行导向滤波、孔洞填充和几何判断后处理，以获取云检测结果。该算法的优势在于：（1）无需人工标注即可自动获取训练样本；（2）能够充分利用近红外波段信息。本文将该算法运用于植被、土壤、湿地和冰雪场景这4种典型场景中，并与目前流行的非监督云检测算法对比分析。相较于其他云检测算法，定性分析结果表明本文云检测结果与人工标注的云分布真实图具有较好的一致性；定量结果表明本文提出的算法在各个场景上都具有最低的错误率。通过以上对比分析，表明本文提出的云检测算法在不同的场景下识别结果都更加准确，证明了本文算法对于HY-1C CZI陆地遥感图像云检测的有效性。
The Coast Zone Imager (CZI) onboard the Chinese first marine aqua-color satellite HY-1C started operational operations in June 2019. The data acquired by CZI have the characteristics of medium resolution
large width and high revisit period and taking into account the requirements of ocean water color
terrestrial ecology and polar glaciers. Therefore
the large amount of coastal
land
and ocean data acquired by CZI is of great significance for marine disaster and environmental monitoring research. However
related studies have shown that clouds cover an average of 68% of the earth's surface. CZI data is severely affected by cloud
which will then have a strong impact on its subsequent applications. The effective identification of clouds in remote sensing images is extremely important for the application of CZI images. Most of the existing cloud detection algorithms are based on RGB images or multi-spectral images including thermal infrared band. There are few researches on cloud detection algorithms for RGB-NIR four-band remote sensing images
such as HY-1C CZI. The objective of this paper is thus to propose an unsupervised cloud detection method for HY-1C CZI remote sensing images that makes full use of NIR band information. The method includes four processes: training samples selection
feature extraction
Support Vector Machine (SVM) classification
and post-processing. In the selection of training samples
combining dark channel reflectivity
normalized vegetation index and whiteness index of the image
this paper proposes an automatic training sample extraction algorithm
which uses the whiteness index to obtain detail information
and accurately extract cloud/non-cloud samples through a gradual refinement process. For feature extraction
the spatial spectrum feature information of CZI remote sensing image is selected
including reflectance
spectral index
texture and structure features
to characterize remote sensing image features
and maximize the feature difference between cloud and non-cloud regions. Based on the above automatically extracted sample and its feature description
SVM is used to initially classify the CZI remote sensing data
and then the guided filtering
hole filling and geometric judgment post-processing are performed to obtain the final high-precision cloud detection results. This paper applies the algorithm to four typical scenarios (vegetation
soil
wetland
and ice and snow scenarios)
and compares and analyzes it with the currently popular unsupervised cloud detection algorithms. Compared with other cloud detection algorithms
the qualitative analysis results show that the cloud detection results in this paper are in good agreement with real cloud distribution image labeled by human. In addition
the most commonly used error rate metric is also used to quantitatively evaluate the cloud detection results. It shows that the error rates of the proposed algorithm in vegetation
soil
wetland
ice and snow scenes are 0.027
0.064
0.026
and 0.049
respectively; and that has the lowest error rate in four scenarios. Through the above comparative analysis
the detection results of the proposed algorithm in different scenarios are more accurate
which demonstrates the effectiveness of the proposed algorithm for cloud detection from HY-1C CZI data.
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