1. 毕业设计（论文）主要目标：
1、基于卷积神经网络算法的卫星云图检测
2、基于卷积神经网络算法的云量计算
2. 毕业设计（论文）主要内容：
该文首先从各个方面介绍了关于国内外对卫星云图的检测和如何进行云量计算的研究，搜集了各方面的文献资料对卫星云图解译展开一个初步的了解。基于卷积神经网络这一方法进行学习研究，首先介绍了卷积神经网络是一个什么样的方法，这一方法的研究历史如何，之前应用与哪些方面；其次是介绍了卷积神经网络的结构；最后介绍了卷积神经网络的算法以及优点。利用了来自HJ-1A/1B的卫星云图数据，然后进行实验，通过采集云图上的数据，组成9000的训练样本，并经过预处理后来作为卷积神经网络的训练样本，用这种方法检测云图，计算出灰度值，通过与阈值法对比，最后验证卷积神经网络的优越性。
3. 主要参考文献
| [1]王毅，国际新一代对地测系统的发展及主要应用，北京：气象出版社 2006 [2]高太长，刘磊，赵世军，等.全天空测云技术现状及发展[J].应用气象学报，2010，21（1）：101-109 [3]Murtagh F,Barreto D,Marcello J.Decision boundaries using Bayes factors:the case of cloud masks[J].IEEE transactions on geoscience and remote sensing,2003,41(12):2952-2958. [4]Long C N,Sabburg J M,Calbo J,et al.Retrieving cloud characteristics from ground-based daytime color all-sky images[J].Journal of Atmospheric and Oceanic Technology 2006,23:633-652. [5]Huo J,Lu D,Cloud determination of all-sky images under low-visibility conditions[J].J Atmos Ocean Tech,2009,26:2172-2181. [6]L.L.Stpwe,P.A.Davis,E.P.Menzel,Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification alogorithm for the Advanced Very High Resolution Radiometer.J Atmos.Ocean Technol,pp.656-681,1999. [7]R.W.Saunders ,K.T.Kriebel,An improved method for detecting clear sky and cloudy radiances from ACHRR data,International journal of Remote Sensing,pp.123-150,1988. [8]S.A. Ackerman, K.Strabala, Discriminating clear sky from clouds with MODIS,J.Geophys.Res,pp:32141-32157,1998. [9]E.S.Maddy,T.S.King,H.Sun,et .al.Using MetOp-A AVHRR Clear-Sky Measurements to Cloud-Clear MetOp-A IASI Column Radiances,J Atmos Ocean Technol,pp.1104-1116,2011. [10]Simpsona J.J,Gobata J.I.Improved cloud detection in GOES scenes over the oceans.Remote Sensing of Environment,1995,52(2):79-94. [11]R,M. Wlch,M.S. Navar,S.K. Sengupta,The effect of spatial Resolution upon Texture-based Cloud Field Classification,J Geophy Res,vol.94,pp.14767-14781,1989. [12]M. Singh, M. Glennen,Automated ground-based cloud recognition, Pattern Anal Appl,vol.8,pp.258-271,2005. [13]K.A.Buch, C.H.Sun,L.R. Thome, Cloud classification whole-sky image data,Atmos Meas Tech,vol.3,pp.269-299,2010. [14]J. Calbo,J. Sabburg,Feature extraction from whole-sky ground-based images for cloud-type recognition,J.Atmos Ocean Technol,vol.25,pp.3-14,2008. [15]Choi H,Bandschedler R. Cloud detection in landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision.Remote Sensing of Environment.2004,91(2):237-242. [16]JA.Heinle, A. Macke, A. Sricastav, Automatic cloud classification of whole sky images,Atmos Meas Tech,vol.3,pp.557-567,2010. [17] I.C. Christodoulos, C. M. Silas, S.P. Constantinos, Multifeature Texture Analysis for the Classification of Clouds in Satellite Imagery, IEEE Trans. Geosci. Remote Sens., 2003, 41(11):2662-2668. [18] E. Kassianov, C.N. Long, M. Ovtchinnikov, Cloud sky cover versus cloud fraction: whole-sky simulations and observations, J. Appl. Meteorol. Clim., 2005, 44(1): 86-98. [19]A. Ghosh,R. N. Pal,J. Das,A fuzzy rule based approach to cloud cover estimation.Remote sensing of environment ,pp.531-549,2006. [20]Simpson J.J. and Humphery C.An automated cloud screening algorit hm for daytime advanced very high resolution radiometer imagery. J. Geophys. Res., 1990. 95(C8): 13 459 -13 4811. [21] G. Luis, B. Lorenzo，Mean Map Kernel Methods for Semi-supervised Cloud Classification, IEEE Trans. Geosci. Remote Sens., 2010, 48(1): 207-220. [22] Y. Lee, G. Wahba, S.A. Ackerman, Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines, J. Atmos. Oceanic Technol., 2004,21:159-164. [23] P. Walder, I. MacLaren, Neural network based methods for cloud classification on AVHRR images, Int. J. Remote Sens., 2000, 21(8):1693-1708. [24] T. McIntire, J. Simpson, Arctic sea ice, cloud, water, and lead classification using neural networks and 1.6 μm data, IEEE Trans. Geosci. Remote Sens., 2002, 40(9):1956–1972. [25] J.A. Arriaza, F. G. Rojas, M. P. Lpez, M. Cantn, An automatic cloud-masking system using backpro neural nets for AVHRR scenes, IEEE Trans. Geosci. Remote Sens., 2003, 41(4): 826–831. [26] J. Lee, R.C. Weger, S.K. Sengupta, R.M. Welch, A neural network approach to cloud classification, IEEE Trans. Geosci. Remote Sens.,1990, 28:846 – 855. [27] Y. Liu, J. Xia, C. Shi, Y, Hong, An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network, Sensors, 2009, 9(7):5558-5579. [28]师春香.吴蓉璋.项续康.多阈值和神经网络卫星云图自动分割实验.应用气象学报.2001，12（1），70-78.
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