H8/AHI卫星数据的夜间陆地雾自动检测

夜间雾已成为交通事故频发的重要隐患，夜间雾检测对防治和减少因雾造成的事故和损失，保障人民生命财产安全具有重要的意义。雾与晴空地表夜间亮温差存在明显差异，二者在亮温差影像上有清晰边缘的特征，算法通过Canny边缘检测获取该边缘混合像元，根据边缘混合像元亮温差均值自动获取二者分离检测阈值，进行夜间陆地雾检测。5天H8/AHI夜间雾算法检测结果平均正确率为93.3%、误警率为29.8%，可靠性因子为67.8%，结果表明算法较适合大面积浓雾检测，但对地表存在的特殊天气如霾、雨雪天、雾发展为低云等情况易虚假报警，如未有相关伴随雾的天气现象出现，检测结果正确率为94.6%、误警率为0.05%、可靠性因子为90.1%。算法优点为可自动确定雾与晴空地表的分离检测阈值，与已有夜间雾自动检测算法相比，该算法检测精度有较大提高。2015年11月27日至12月1日17:00～07:00不同时刻夜间雾时序检测定性验证结果证明，算法适合晨昏时刻遥感影像中已处于夜晚区域的雾检测，可检测出90%左右的雾区；对整幅影像均处于夜间的遥感影像，算法检测结果正确率高达90%以上，定性验证结果进一步证明了算法的稳定性和可靠性。

Automatic Detection of Night Land Fog based on H8/AHI Satellite Data

Night fog has become an important hidden danger of frequent traffic accidents. Night fog detection is of great significance to prevent and reduce accidents and losses caused by fog， and protect the safety of people's lives and property. There is an obvious boundary between fog and surface of clear sky in the nighttime brightness temperature difference image. Canny edge detection is used to obtain the edge mixed pixel， and the separation detection threshold is automatically obtained by the average brightness temperature difference value of the edge mixed pixel to detect the night land fog. The results of 5-day H8/AHI night fog detection show that the probability of detection is 93.3%， the false alarm ratio is 29.8% and the critical success index is 67.8%. The results show that the algorithm is more suitable for large area dense fog detection， and it is prone to false alarm for special weather such as haze， rainy and snowy days， and fog developing into low cloud. If there is no weather phenomenon associated with fog， the probability of detection is 94.6%， the false alarm ratio is 0.05%， and the critical success index is 90.1%. This algorithm can automatically determine the separation detection threshold of fog and clear sky surface. Compared with the existing automatic detection algorithms of night land fog， this algorithm has higher detection accuracy. The qualitative verification results of night land fog timing detection at different times from 17：00~07：00 on November 27， 2015 to December 1， 2015 show that the algorithm is suitable for the fog detection in the night area of the remote sensing image at dawn and dusk， which can detect about 90% of fog area； for the whole image at night， the algorithm can detect more than 90% of fog area. The results of qualitative verification further prove the stability and reliability of the algorithm.