We propose here a fuzzy hybrid methodology for the classification, conceived as a cognitive process, of remote sensing images. The salient aspect of the approach is the combined use of different techniques: the linear mixture model, a supervised fuzzy statistical classifier and a fuzzy labeling technique, An application for the identification of rice crops in a Landsat Thematic Mapper image has been developed with the aim of experimentally evaluating the performance of the overall strategy in a real domain where fuzzy membership to classes are essential in class discrimination, The results have then been compared with those obtained by means of the Maximum Likelihood classifier.
A hybrid approach to fuzzy land cover classification
PA Brivio;A Rampini;
1996
Abstract
We propose here a fuzzy hybrid methodology for the classification, conceived as a cognitive process, of remote sensing images. The salient aspect of the approach is the combined use of different techniques: the linear mixture model, a supervised fuzzy statistical classifier and a fuzzy labeling technique, An application for the identification of rice crops in a Landsat Thematic Mapper image has been developed with the aim of experimentally evaluating the performance of the overall strategy in a real domain where fuzzy membership to classes are essential in class discrimination, The results have then been compared with those obtained by means of the Maximum Likelihood classifier.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.