光学学报, 2020, 40 (16): 1628001, 网络出版: 2020-08-07   

CDAG改进算法及其在GF-6 WFV数据云检测中的应用 下载: 741次

CDAG-Improved Algorithm and Its Application to GF-6 WFV Data Cloud Detection
作者单位
山东科技大学测绘科学与工程学院, 山东 青岛 266590
摘要
为提高GF-6 WFV数据的利用效率,研究了GF-6 WFV数据的云检测算法。该算法是基于阈值自动生成的云检测(CDAG)算法,通过挖掘云和典型地表在可见光、近红外波段的光谱差异信息,实现了对多光谱卫星传感器的云检测。考虑到GF-6 WFV数据光谱范围相对较窄,云与亮地表的识别能力相对较弱的问题,加入了离差指数和亮地表指数,使用更多的波段组合方式,更加深入地分析云像元和晴空像元的差异,提高了典型地表与云的识别精度。通过遥感目视判读的方法对本研究中抽取的不同子区域的云检测结果进行分析,识别精度达到85.16%,漏分误差和错分误差分别为14.84%和2.39%,实现了较高的识别精度。
Abstract
To utilize GF-6 WFV data more efficiently, the cloud detection algorithm, which is based on cloud detection algorithm-generating (CDAG) algorithm, is investigated in this study. The proposed method can effectively realize high-precision cloud detection of multi-spectral satellite sensors by completely mining the spectral difference information of the cloud and the typical surface in visible and near-infrared bands. Considering that the spectral range of GF-6 WFV is relatively narrow, and the recognition ability of the cloud and the bright surface is relatively weak, we add the dispersion index and bright surface index, and use more band combinations to further analyze the differences between cloud and clear pixels so as to improve the recognition accuracy of typical surface and cloud. Cloud detection results from different sub-regions are varified through remote visual interpretation, which suggests that the overall accuracy reaches 85.16%, 14.84% of clouds are not identified, and 2.39% of the surface is incorrectly identified as clouds, thereby demonstrating the proposed method can achieve high recognition accuracy.

董震, 孙林, 刘喜荣, 王永吉, 梁天辰. CDAG改进算法及其在GF-6 WFV数据云检测中的应用[J]. 光学学报, 2020, 40(16): 1628001. Zhen Dong, Lin Sun, Xirong Liu, Yongji Wang, Tianchen Liang. CDAG-Improved Algorithm and Its Application to GF-6 WFV Data Cloud Detection[J]. Acta Optica Sinica, 2020, 40(16): 1628001.

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