High resolution satellite precipitation estimation based on cloud classification
Journal of Aeronautics & Aerospace Engineering

Journal of Aeronautics & Aerospace Engineering
Open Access

ISSN: 2168-9792

High resolution satellite precipitation estimation based on cloud classification

3rd International Conference and Exhibition on Satellite & Space Missions

May 11-13, 2017 Barcelona, Spain

Nicolas H Younan

Mississippi State University, USA

Keynote: J Aeronaut Aerospace Eng

Abstract :

Satellite precipitation estimation at high spatial and temporal resolutions is beneficial for research and applications in the areas of weather, flood forecasting, hydrology and agriculture. In this presentation, we incorporate advanced image processing and pattern recognition tools into the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) methodology to enhance satellite precipitation and rainfall estimation. The enhanced algorithm incorporates five main steps to derive precipitation estimates: Segmenting the satellite infrared cloud images into patches; extracting features from the segmented cloud patches; feature selection or dimensionality reduction; categorizing the cloud patches into separate groups and; obtaining a relationship between the brightness temperature of cloud patches and the rain-rate (T-R) for every cluster. In addition to the features utilized for cloud patch classification, wavelet and lightning features are also extracted. Both feature selection and dimensionality reduction techniques are used to reduce the dimensionality as well as diminish the effects of the redundant and irrelevant features. A variety of feature selection techniques, such as feature similarity selection and a filter-based feature selection using genetic algorithm are examined and the Entropy Index (EI) fitness function is used to evaluate the feature subsets. Furthermore, independent component analysis was examined and compared to other linear and nonlinear unsupervised dimensionality reduction techniques to reduce the dimensionality and increase the estimation performance. The results show that the enhanced algorithm incorporating the above techniques improves precipitation estimation.

Biography :

Nicolas H Younan is completed his BS and MS at Mississippi State University, in 1982 and 1984, respectively, and PhD at Ohio University in 1988. His research interests include Signal Processing and Pattern Recognition. He has been involved in the development of advanced signal processing and pattern recognition algorithms for data mining, data fusion, feature extraction and classification, and automatic target recognition/identification. He has published over 250 papers in refereed journals, conference proceedings, and book chapters. He served as the General Chair and Editor for the 4th IASTED International Conference on Signal and Image Processing; Co-editor for the 3rd International Workshop on the Analysis of Multi-Temporal Remote Sensing Images; Guest Editor of Pattern Recognition Letters, and Co-chair of Workshop on Pattern Recognition for Remote sensing (2008-2010).

Email: [email protected]