REMOTE SENSINGMONITORING TECHNOLOGY OF THICK FOG AT NIGHT BASED ON NEURAL NETWORKS AND FRACTAL GRAIN

Zhang Shunqian; Yang Xiurong

Vol.16, NO.6, 2005

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2005 > 16(6): 804-809

Zhang Shunqian, Yang Xiurong. Remote sensing monitoring technology of thick fog at night based on neural networks and fractal grain. J Appl Meteor Sci, 2005, 16(6): 804-809.

Citation:

Zhang Shunqian, Yang Xiurong. Remote sensing monitoring technology of thick fog at night based on neural networks and fractal grain. J Appl Meteor Sci, 2005, 16(6): 804-809.

Zhang Shunqian, Yang Xiurong. Remote sensing monitoring technology of thick fog at night based on neural networks and fractal grain. J Appl Meteor Sci, 2005, 16(6): 804-809.

Citation:

Zhang Shunqian, Yang Xiurong. Remote sensing monitoring technology of thick fog at night based on neural networks and fractal grain. J Appl Meteor Sci, 2005, 16(6): 804-809.

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REMOTE SENSINGMONITORING TECHNOLOGY OF THICK FOG AT NIGHT BASED ON NEURAL NETWORKS AND FRACTAL GRAIN

Sichuan Provincial Agrometeorological Center, Chengdu 610071

Received Date: 2005-03-24
Rev Recd Date: 2005-06-06
Publish Date: 2005-12-31

Abstract

Abstract

In terms of the ground-object spectrum information and the image-grain information as symbols to distinguish the ground-objects, fractal theory and BP neural networks are used to monitor thick fog at night, which increases the monitoring precision of thick fog obviously. Compared with the traditional Maximum Likelihood Classifying (MLC), the identified precision of clear sky ground, fog areas, cloud areas is increased, especially that of the cloud areas is increased by 10%. The image-grain extraction technology of grey-power box-counting dimension basing on grey degree connected region made extracting the borderline of cloud and fog more reasonable. Also, the merger of kings is discussed。
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References(9)

Figures and Tables

图 1 三层感知器BP 神经网络拓扑结构

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图 2 2004 年11 月17 日06:00 四川盆地雾区遥感监测图

(a)灰度图，(b)BP 分类法雾区监测图，(c)类归并后的雾区图

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表 1 MLC与BP 分类法精度对比表

References

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