Skip to main content

An evaluation of fuzzy classifications from IRS 1C LISS III imagery: a case study

Buy Article:

$63.00 plus tax (Refund Policy)

Remote sensing data are frequently used to produce crisp and fuzzy classifications for land cover applications. Fuzzy approaches are attractive for classification of images dominated by mixed pixels. Recently, fully-fuzzy classification that can incorporate mixed pixels in all the three stages of a supervised classification has been recommended. In this Letter, the results of a case study on fully-fuzzy classification of Indian Remote Sensing (IRS) 1C Linear Imaging Self Scanning Sensor (LISS) III imagery are reported. The results illustrate that fully-fuzzy classification produces more accurate land-cover mapping than the conventional crisp classification. For instance, the areal extents of three dominant classes (i.e. agriculture, forest and grass) obtained from fully-fuzzy classification differ by only 13% from the actual areal extents, compared to 34% difference in area observed from crisp classification.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: School of Engineering and Applied Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK 2: Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee-247 667, Uttaranchal, India

Publication date: 2003-08-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more