Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique
暂无分享,去创建一个
Thomas Nauss | Christoph Reudenbach | Hanna Meyer | Meike Kühnlein | C. Reudenbach | T. Nauss | H. Meyer | Meike Kühnlein
[1] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[2] Aixia Guo,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2014 .
[3] Catherine Prigent,et al. Precipitation retrieval from space: An overview , 2010 .
[4] S. Sorooshian,et al. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .
[5] Boualem Haddad,et al. Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images , 2016 .
[6] Johannes Schmetz,et al. Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation , 2001 .
[7] Donny M. A. Aminou,et al. Characteristics of the Meteosat Second Generation (MSG) radiometer/imager: SEVIRI , 1997, Remote Sensing.
[8] PRECIPITATION ESTIMATION BASED ON SPECTRAL AND TEXTURAL FEATURES OF METEOSAT MULTISPECTRAL INFRARED DATA , 2012 .
[9] Y. Hong,et al. Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .
[10] G. Visconti,et al. A Neural Network Approach to Real-Time Rainfall Estimation for Africa Using Satellite Data , 2003 .
[11] B. J. Conway,et al. Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks , 2005 .
[12] Christina Gloeckner,et al. Modern Applied Statistics With S , 2003 .
[13] Andreas Stolcke,et al. A study in machine learning from imbalanced data for sentence boundary detection in speech , 2006, Comput. Speech Lang..
[14] Zhao-Liang Li,et al. Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image , 2014, ATMOS 2014.
[15] Jörg Bendix,et al. Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data , 2007 .
[16] Gaëtan Kerschen,et al. Feature extraction using auto-associative neural networks , 2004 .
[17] Alexandros G. Charalambides,et al. Equipment and methodologies for cloud detection and classification: A review , 2013 .
[18] Tim Appelhans,et al. Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests , 2014 .
[19] A. Gruber,et al. GOES Multispectral Rainfall Algorithm (GMSRA) , 2001 .
[20] S. Ameur,et al. Cloud classification using the textural features of Meteosat images , 2004 .
[21] Lutz Hamel,et al. Model Assessment with ROC Curves , 2009, Encyclopedia of Data Warehousing and Mining.
[22] Thomas Nauss,et al. Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data , 2006 .
[23] S. K. Sengupta,et al. Cloud field classification based upon high spatial resolution textural features: 1. Gray level co‐occurrence matrix approach , 1988 .
[24] Max Kuhn,et al. Applied Predictive Modeling , 2013 .
[25] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[26] M. Gevrey,et al. Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .
[27] Jörg Bendix,et al. Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data , 2008 .
[28] M. Lazri,et al. Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall , 2014 .
[29] V. Levizzani,et al. Status of satellite precipitation retrievals , 2009 .
[30] Tim Appelhans,et al. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals , 2015 .
[31] Michael Uddstrom,et al. Satellite Cloud Classification and Rain-Rate Estimation Using Multispectral Radiances and Measures of Spatial Texture , 1996 .
[32] Haralambos Feidas,et al. Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data , 2011, Theoretical and Applied Climatology.
[33] H. Feidas,et al. DETECTION OF RAINY CLOUDS BASED ON THEIR SPECTRAL AND TEXTURAL FEATURES ON METEOSAT MULTISPECTRAL INFRARED DATA , 2011 .
[34] H. Feidas,et al. Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data , 2013, Theoretical and Applied Climatology.
[35] Max Kuhn,et al. caret: Classification and Regression Training , 2015 .
[36] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[37] Tim Appelhans,et al. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI , 2014 .
[38] Pierre Roudier,et al. Mapping Daily Air Temperature for Antarctica Based on MODIS LST , 2016, Remote. Sens..
[39] Kuolin Hsu,et al. PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis , 2009 .
[40] Kuolin Hsu,et al. Evaluating the Utility of Multispectral Information in Delineating the Areal Extent of Precipitation , 2009 .
[41] F. Marzano,et al. Artificial neural-network technique for precipitation nowcasting from satellite imagery , 2006 .
[42] Silas C. Michaelides,et al. Multifeature texture analysis for the classification of clouds in satellite imagery , 2003, IEEE Trans. Geosci. Remote. Sens..
[43] G. H. Rosenfield,et al. A coefficient of agreement as a measure of thematic classification accuracy. , 1986 .
[44] Francesco Meneguzzo,et al. A Review of Satellite-based Rainfall Estimation , 2002 .
[45] Jörg Bendix,et al. Satellite based remote sensing of weather and climate: recent achievements and future perspectives , 2011 .