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遥感技术与应用  2015, Vol. 30 Issue (4): 714-718    DOI: 10.11873/j.issn.1004-0323.2015.4.0714
图像与数据处理     
基于BP神经网络的云相态检测方法研究
熊贤成,杨春平,敖明武,郭晶,曾丹丹
(电子科技大学光电信息学院,四川 成都 610054)
A Research on Cloud Phase Detection based on BP Neural Network
Xiong Xiancheng,Yang Chunping,Ao Mingwu,Guo Jing,Zeng Dandan
(School of Opto\|Electronic Information,University of Electronic Science and Technology of China,Chengdu 610054,China)
 全文: PDF(1523 KB)  
摘要:

利用MODIS中5个光谱波段上不同云相态的特性,提出了一种基于BP神经网络的云相态检测方法。首先,分析了所选波段上不同云相态的特性,利用5个波段上光谱图像的反射率、亮温值和亮温差值构成4组特征数据作为输入层,隐层和输出层分别采用优化的传输函数。然后,利用3层前馈型BP神经网络对所选波段MODIS数据进行了云相态检测。最后,将两组测试数据用该BP神经网络算法进行云相态检测的结果与相应MOD06云相态数据进行了对比分析,结果表明该方法能很好地识别云相态,检测平均准确率达到86.11%,计算结果与标准结果平均相关性达到0.874的高度相关,且无需在计算前进行复杂的云和晴空分离处理。

关键词: MODIS神经网络云相态BP算法    
Abstract:

To improve the image quality of band 5 and band 27 which contain stripe noises acquired by Moderate Resolution Imaging Spectroradiometer (MODIS) level_1B,based on MODIS scanning characteristics,a method of using the max mean of each swath to judge the stripe noises was proposed.When destriping noises,according to the thought of single line stripe interpolation on band 5,an interpolate method of using the adjacent multi\|line stripe noises on band 27 was proposed.Finally,comparison diagram,mean diagram and numeric analysis between original data and processed data were compared to validate the effect of destriping noises.The results show that the method can judge all the stripe noises exactly on both bands,and can remove the stripe noises well.The process of destriping noises is easily and suitable for the complex remote sensing scenes.

Key words: MODIS    Neural network    Cloud phase    BP arithmetic
收稿日期: 2014-01-16 出版日期: 2015-09-22
:  TP 79  
基金资助:

总装预研基金项目(9140A03040809DZ02),国家自然科学基金项目(11173008)。

通讯作者: 杨春平(1966-),男,重庆万州人,高级工程师,博士,主要从事目标与环境的光学特性研究。 Email:cpin2@163.com。    
作者简介: 熊贤成(1989-),男,湖北仙桃人,硕士研究生,主要从事大气遥感研究。 Email:xcxiong08@126.com。
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引用本文:

熊贤成,杨春平,敖明武,郭晶,曾丹丹. 基于BP神经网络的云相态检测方法研究[J]. 遥感技术与应用, 2015, 30(4): 714-718.

Xiong Xiancheng,Yang Chunping,Ao Mingwu,Guo Jing,Zeng Dandan. A Research on Cloud Phase Detection based on BP Neural Network. Remote Sensing Technology and Application, 2015, 30(4): 714-718.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.4.0714        http://www.rsta.ac.cn/CN/Y2015/V30/I4/714

[1]Matthew D.Clouds at Arctic Atmospheric Observatories.Part II:Thermodynamic Phase Characteristics[J].Journal of Applied Meteorology and Climatology,2011,50:645-661.

[2]Wouter H,Piet S,Robert B.Cloud Thermodynamic-phase Determination from Near-infrared Spectra of Reflected Sunlight[J].Journal of the Atmospheric Science,2002,59:83-96.[3]Zeng S,Riedi J,Parol F,et al.An Assessment of Cloud Top Thermodynamics Phase Products Obtained from a-train Passive and Active Sensors[EB/OL].http://www.atmos-meas-tech-discuss.net/6/8371/2013/amtd-6-8371-2013.html,2013,2014.

[4]Myoung C,Shaima L,Ping Y.Application of CALIOP Measurements to the Evaluation of Cloud Phase Derived from MODIS Infrared Channels[J].Journal of Applied Meteorology and Climatology,2009,48:2169-2180.

[5]Sheng Xia,Sun Longxiang,Zheng Qingmei.Simulated Annealing Optimized BP-ANN Method for Cloud Thermodynamic Phase Retrieval[J].Journal of PLA University of Science and Technology,2008,9(1):98-102.[ 盛夏,孙龙祥,郑庆梅.模拟退火优化BP神经网络进行云相态分类[J].解放军理工大学学报,2008,9(1):98-102.]

[6]Liu Yujie,Yang Zhongdong,et al.The Theory and Algorithm of MODIS Remote Sensing Information Processing[M].Beijing:Science Press,2001.[刘玉洁,杨忠东,等.MODIS遥感信息处理原理与算法[M].北京:科学出版社,2001.]

[7]Bryan A,Peter F,Kathleen I,et al.Remote Sensing of Cloud Properties Using MODIS Airborne Simulator Imagery during SUCCESS[J].Journal of Geophysical Research,2000,105:11781-11792.

[8]Jeffrey R,Janet M.Cloud Particle Phase Determination with the AVHRR[J].Journal of Applied Meteorology,2000,39:1797-1804.

[9]Dong Changhong.Matlab Neural Network and Application[M].Beijing:National Defense Industry Press,2007.[董长虹.Matlab神经网络与应用[M].北京:国防工业出版社,2007.]

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