Fast extracting of change area from remote sensing image by Fuzzy theory and case base reasoning

Ting Shiuan Wang, Teng To Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study presents the technology to combine the remote sensing image of SPOT and FORMOSAT-2 satellite image by Fuzzy theory and case base reasoning. This method adopt three experience identify factors of NDVI, shape, and color to establish the membership function. The Fuzzy theory was applied to estimate the process of thinking as the human brain; while the Case Base Reasoning method was used to increase the capability of self-loop learning and support its consistency with the real nature. The results show that the successful rate of identification was between 90 percent. The Case Base Reasoning results show that the two data similarity was between 46 percent. The Fuzzy and Case Base Reasoning difference factor was (satellite sensors, inclination, date, shadowing, etc.). The rate can be increase if there is enough experienced data. It reveal that fuzzy theory with case base reasoning indeed can rapid screen the change area from remote sensing image in before and after the disaster event.

Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Pages340-345
Number of pages6
DOIs
Publication statusPublished - 2011 Sep 27
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: 2011 Jun 272011 Jun 30

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
CountryTaiwan
CityTaipei
Period11-06-2711-06-30

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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