Neuro-fuzzy approach for monitoring global snow and ice extent with the SSM/I

Changyi Sun, Christopher M.U. Neale, Heng Da Cheng

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

A neuro-fuzzy approach for classification of snow cover and sea ice from the Special Sensor Microwave/Imager (SSM/I) satellite data is presented. The fuzzy c-means algorithm was used as a supervised clustering method for mapping the SSM/I data of known classes into fuzzy c-partitions from which to determine the fuzzy c-means for each class. A single-hidden-layer neural network was implemented to learn the fuzzy c-means and desired responses in terms of fuzzy memberships. After training, the neural network was applied as a fuzzy membership function to fuzzify any inputs of unknown SSM/I data and defuzzify the outputs into crisp classes for mapping snow and ice extent. Weekly snow and sea ice extent was tested and compared with the one derived from the National Snow and Ice Data Center (NSIDC).

Original languageEnglish (US)
Pages1274-1276
Number of pages3
StatePublished - 1998
Externally publishedYes
EventProceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5) - Seattle, WA, USA
Duration: Jul 6 1998Jul 10 1998

Other

OtherProceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5)
CitySeattle, WA, USA
Period7/6/987/10/98

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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