Fuzzy min-max neural network for satellite infrared image clustering

Author

Goswami, Bhaswati ; Bhandari, Gupinath ; Goswami, Suparna

Author_Institution

Dept. of Comput. Applic., Narula Inst. of Technol., Kolkata, India

fYear

2012

fDate

Nov. 30 2012-Dec. 1 2012

Firstpage

239

Lastpage

242

Abstract

The process of estimation of precipitation from satellite images begins with the detection and identification of convective clouds. Clustering of the satellite infrared images is required in order to estimate the cloud cover area. In this paper a neuro-fuzzy technique in the form of unsupervised fuzzy minmax clustering neural (FMMCN) network has been implemented for clustering satellite infrared image. Each cluster is in the form of an n-dimensional hyperbox defined by minimum and maximum points and a fuzzy membership function. FMMCN suits this application area because it is completely unsupervised and hence, unlabeled data can be used with it. Also the number of clusters is not required to be mentioned at the beginning as it is calculated dynamically.

Keywords

clouds; fuzzy neural nets; fuzzy set theory; geophysical image processing; infrared imaging; minimax techniques; pattern clustering; cloud cover area; convective cloud detection; convective cloud identification; fuzzy membership function; fuzzy min-max neural network; n-dimensional hyperbox; neuro-fuzzy technique; precipitation estimation process; satellite infrared image clustering; unsupervised FMMCN; Clouds; Clustering algorithms; Image segmentation; Indexes; Labeling; Neural networks; Satellites; clustering; convective cloud; fuzzy cluster validity index; fuzzy min-max neural network; infra red images; k-means;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Applications of Information Technology (EAIT), 2012 Third International Conference on

Conference_Location

Kolkata

Print_ISBN

978-1-4673-1828-0

Type

conf

DOI

10.1109/EAIT.2012.6407913

Filename

6407913