International Journal of Remote Sensing · 2003 · 41 citations · 0 references
Precision AgricultureEngineeringIrs 1CLand UseFuzzy ClassificationsLand CoverCrisp ClassificationEarth ScienceSocial SciencesImage AnalysisData SciencePattern RecognitionConventional Crisp ClassificationFuzzy Pattern RecognitionFuzzy LogicFuzzy ComputingSoil ClassificationGeographyLand Cover MapFuzzy MathematicsRemote SensingCover MappingLiss Iii ImageryFuzzy ClusteringIndian Remote Sensing
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.