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摘要： 高分四号卫星是我国第一颗地球同步轨道遥感卫星，以其高频、宽幅的特点，可为我国农业、林业、减灾、气象、环保和水利等应用提供快速、稳定的光学遥感影像，高效的影像自动云检测有助于进一步提高高分四号影像的利用效率。CDAG（Cloud Detection Algorithm-Generating）是一种基于像元组分光谱分析的自动云检测算法，能有效降低混合像元、复杂表面结构和大气等因素的影响。为了探索CDAG算法对于高分4号多光谱影像（GF4-PMS）的云检测应用能力，首先，从高光谱影像（AVIRIS）上选取不同的云类型和各种地表覆盖类型，建立云像元库和地物像元库；其次，基于高光谱像元库和GF4-PMS传感器光谱响应函数模拟出多光谱影像像元库；然后，根据碎云、薄云、厚云及非云像元的光谱差异性分析，将GF4-PMS影像的待检测像元与终端像元进行相似概率分析，实现基于最佳阈值自动迭代的GF4-PMS影像云检测；最后，从云像元正确率、晴空像元正确率、误判率、漏判率等多个指标进行云检测精度验证。结果表明：AVIRIS影像可以有效提取适用于GF4-PMS影像云检测的终端像元库，基于CDAG算法能较好地识别GF4-PMS影像上各种类型的云，对于不同时相、不同下垫面的碎云、薄云、厚云的检测精度可达90%以上。因此，基于先验终端像元库的云检测法对于提升GF4-PMS影像的利用效率具有较好的应用价值。 关键词： CDAG算法;  GF4-PMS;  云检测;  像元库;  数据模拟     Abstract: The GaoFen4 (GF4) satellite is China’s first geo-synchronous orbit remote sensing satellite. With the advantages of high frequency and wide width, it can provide fast and stable optical remote sensing images for agricultural, forestry, disaster reduction, meteorology, environmental protection, water conservancy and other applications in China. Efficient image automatic cloud detection helps to further improve the utilization efficiency of GaoFen4 images. CDAG（Cloud Detection Algorihtm-Generating）Cloud detection is an automatic cloud detection algorithm based on spectral analysis of pixel components, which can effectively reduce the influence of mixed pixels, complex surface structure and atmosphere. This paper aims to explore the application of CDAG algorithm in cloud detection of GaoFen4 multispectral imagery (GF4-PMS). Firstly, different cloud types and surface cover types were selected from hyperspectral images (AVIRIS) to establish cloud pixel library and clear sky pixel library. Next, the pixel library of multispectral images was simulated based on Hyperspectral pixel library and spectral response function of GF4-PMS sensor. Then, according to the spectral difference analysis of broken cloud, thin cloud, thick cloud and non-cloud pixel, the similarity probability analysis was performed on the to-be-detected pixel of the GF4-PMS image and the terminal pixel, and the GF4-PMS image cloud detection based on the optimal threshold automatic iteration was realized. Finally, cloud detection accuracy verification was carried out from multiple indicators such as cloud pixel correct rate, clear sky pixel correct rate, false positive rate and missed rate. The results show that AVIRIS images can effectively extract terminal pixel libraries for GF4-PMS image cloud detection. Clouds of Various types on GF4-PMS images can be better identified based on the CDAG algorithm. The accuracy of detection results for broken clouds, thin clouds and thick clouds with different time phases and different underlying surfaces can reach more than 90%. Therefore, the cloud detection method based on the priori terminal pixel library has a good application value for improving the utilization efficiency of GF4-PMS images.

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