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

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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.
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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: August 1, 2003

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