The Research on Day Fog Detection Using FY2E Data
The traditional fog detection methods based on remote sensing mainly used polar-orbiting satellite data (MODIS, AVHRR) to establish the fog detection model, but the transit time are always later and the time resolution are about one day, so they cannot be good to meet the requirements of fog detection. FY2E (a geostationary satellite) data will be chosen to build the day fog detection model for its high time resolution (one hour) and relatively rich spectrum. In this paper object-oriented thinking and texture differences between fog and cloud were introduced to the fog detection model. According to the simulation results of streamer radiative transfer model based on FY2E data, Snow Separation Index (SSI) will be built to extract snow from fog and Cloud Separation Index (CSI) will be built to extract low clouds from fog. A day fog detection model for FY2E data will be built based on object-oriented thinking and several characteristic parameters. The experiments shows that the fog detection model proposed in this paper achieved good results.
FY2E Fog Object-oriented SSI CSI
Wei Li Liangming Liu Juan Du
School of Remote Sensing and Information Engineering, Wuhan Univ., No.129 Luoshi Road,Wuhan, Hubei, School of Remote Sensing and Information Engineering, Wuhan Univ., No.129 Luoshi Road, Wuhan, Hubei,
国际会议
桂林
英文
1-7
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）