光学学报, 2020, 40 (21): 2128001, 网络出版: 2020-10-26
基于改进LCCD算法的高分六号WFV数据云检测研究 下载: 911次
GF-6 WFV Data Cloud Detection Based on Improved LCCD Algorithm
摘要
高分六号WFV是搭载在我国高分六号卫星上的高空间分辨率多光谱传感器,该传感器实现了高分辨率和宽覆盖的结合。精确识别高分六号WFV数据的云像元对于农业资源监测、林业资源调查以及防灾减灾等行业具有重要意义。基于全球土地覆盖产品FROM-GLC10数据,改进地表类型支持的云检测算法(LCCD算法),开展了高分六号WFV数据的云检测工作。以FROM-GLC10作为先验数据,充分考虑不同地表类型反射率的变化,在每种地表类型上分别采用不同的方法设置阈值。通过目视解译的方法对云检测结果进行精度评价,云正确率整体达到了92.46%,其中植被类、水体类、高亮地表类的云正确率分别为93.09%、95.60%和88.70%。结果表明,改进的基于地表类型的云检测算法有效提高了高分六号WFV数据云检测的精度。
Abstract
GF-6 WFV is a high-spatial resolution multi-spectral sensor loaded on Chinese GF-6 satellite, which realizes the combination of high spatial resolution and wide coverage. Accurately identifying the cloud pixels of GF-6 WFV data is of great significance for supporting agricultural resources monitoring, forestry resources investigation, disaster prevention and mitigation and other industry applications. Based on the global land cover product—FROM-GLC10 (Finer Resolution Observation and Monitoring-Global Land Cover 10) data, the LCCD (Land Cover-based Cloud Detection) algorithm is improved to carry out cloud detection of GF-6 WFV data in the paper. Taking FROM-GLC10 data as a priori data, fully considering the change of reflectivity of different surface types, different methods are used to set thresholds for each surface type. The accuracy of cloud detection results was evaluated by visual interpretation, and the cloud accuracy rate as a whole reached 92.46%, among which the cloud accuracy rates of vegetation type, water type and highlighted surface type were 93.09%, 95.60% and 88.70%, respectively. The results show that the improved cloud detection algorithm based on surface type effectively improves the accuracy of cloud detection of GF-6 WFV data.
王永吉, 明艳芳, 梁天辰, 周雪莹, 贾臣, 王权. 基于改进LCCD算法的高分六号WFV数据云检测研究[J]. 光学学报, 2020, 40(21): 2128001. Yongji Wang, Yanfang Ming, Tianchen Liang, Xueying Zhou, Chen Jia, Quan Wang. GF-6 WFV Data Cloud Detection Based on Improved LCCD Algorithm[J]. Acta Optica Sinica, 2020, 40(21): 2128001.