摘要
针对使用阈值方法反演云相态存在的不足,本文提出了一种基于Self-Organizing Feature Map(SOFM)神经网络的云相态反演方法。采用覆盖中国地域的Feng Yun-3A/Visible and Inf Rared Radiometer(FY-3A/VIRR)多光谱图像开展了云相态反演实验。实验结果表明:SOFM神经网络方法与K-means方法的结果具有较好的一致性,且SOFM神经网络方法反演云相态的准确性优于FY-3A业务产品。此外,SOFM神经网络方法反演云相态所需时间仅为FY-3A业务产品的约1/3。
To address problems of cloud phase retrieval using the threshold method, a cloud phase retrieval approach based on Self-Organizing Feature Map（SOFM） neural network was proposed. Cloud phase retrieval experiments were conducted using Feng Yun-3A/Visible and Inf Rared Radiometer（FY-3A/VIRR） multi-channel images, which cover the China＇s territory. Experiment results indicated that the results from the SOFM neural network approach and the K-means method have good consistency, and the retrieval accuracy of the SOFM neural network exceeds that of the FY-3A operational product. Additionally, retrieval time consumed by the SOFM neural network approach is only about one third of that of the FY-3A operational product.
出处
《光电工程》
CAS
CSCD
北大核心
2015年第12期20-24,共5页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(11173008)
中央高校基本科研业务费专项资金资助项目(103.1.2E022050205)
