| 基于神经网络和分形纹理的夜间浓雾遥感监测技术 |
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| 引用本文： | 张顺谦,杨秀蓉.基于神经网络和分形纹理的夜间浓雾遥感监测技术[J].应用气象学报,2005,16(6):804-809. |
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| 作者姓名： | 张顺谦 杨秀蓉 |
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| 作者单位： | 四川省农业气象中心, 成都 610071 |
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| 基金项目： | 中国气象局成都高原气象研究所开放实验室基金资助 |
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| 摘 要： | 利用地物光谱信息和图像纹理信息作为地物分类识别标志，将分形理论和BP神经网络应用于夜间浓雾的遥感监测，使夜间浓雾的监测精度明显提高。与传统最大似然法(MLC)比较，晴空地表、雾区、云区的识别精度均有提高，特别是云区的识别精度提高了10%，基于灰度连通域的灰度加权计盒维数图像纹理提取技术使云雾边界的提取更加合理，文章最后对类的归并作了讨论。
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| 关 键 词： | BP神经网络 分形理论 云雾分离 |
| 收稿时间： | 2005-03-24 |
| 修稿时间： | 2005-06-06 |
Remote Sensing Monitoring Technology of Thick Fog at Night Based on Neural Networks and Fractal Grain |
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| Zhang Shunqian,Yang Xiurong.Remote Sensing Monitoring Technology of Thick Fog at Night Based on Neural Networks and Fractal Grain[J].Quarterly Journal of Applied Meteorology,2005,16(6):804-809. |
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| Authors: | Zhang Shunqian Yang Xiurong |
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| Affiliation: | Sichuan Provincial Agrometeorological Center, Chengdu 610071 |
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| 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 kinds is discussed. |
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| Keywords: | BP neural networks Fractal theory Cloud and fog separate |
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