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русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 4, pp. 265-275

Classification of cirrus clouds according to MODIS data by fuzzy neural network

V.G. Astafurov1,2  , S.V. Axyonov3  , T.V. Evsyutkin1 
1 V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, Russia
2 Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia
3 National Research Tomsk Polytechnic University, Tomsk, Russia
A classification of cirrus clouds by subtypes according to MODIS data with the spatial resolution of 250 m is considered. Cloud patterns in satellite data are analyzed using information on their texture, obtained with the help of four methods: Gray-Level Co-occurrence Matrix, Gray-Level Difference Vector, Sum and Difference Histograms, and Spectral Features. We present a method capable of determining the set of informative texture features for classifying the cirrus cloud subtypes on the basis of comparative analysis of histograms of their sampling values. Based on studies performed, for each cirrus cloud subtype we created a set of key features which, together with histograms, represent the model of patterns of cirrus cloud subtypes, used for their recognition. We consider the architecture of neural network on the basis of fuzzy logic, which makes it possible to refer the classified pattern to more than one class with different confidence degrees. The network is constructed on a few sub-networks, each focused on one of the cirrus cloud subtypes. The network is learned using a genetic algorithm with different methods for initializing the membership function. The results obtained allow us to state large inhomogeneity of cirrus cloud cover on satellite data and possibility of transitions between cloud subtypes. The validity of classification results is discussed.
Keywords: cirrus clouds, texture features, neural network, fuzzy logic, classification
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