Title :
Fuzzy supervised classification of remote sensing images
Author_Institution :
Dept. of Comput. Sci., Waterloo Univ., Ont., Canada
fDate :
3/1/1990 12:00:00 AM
Abstract :
A fuzzy supervised classification method in which geographical information is represented as fuzzy sets is described. The algorithm consists of two major steps: the estimate of fuzzy parameters from fuzzy training data, and a fuzzy partition of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical parameters to be generated, resulting in a higher classification accuracy. Results of classifying a Landsat MSS image are presented, and their accuracy is analyzed
Keywords :
computerised pattern recognition; computerised picture processing; fuzzy set theory; geographic information systems; geophysics computing; remote sensing; Landsat MSS image; fuzzy sets; fuzzy supervised classification method; geographical information; pixels; remote sensing images; Clustering algorithms; Data mining; Data processing; Fuzzy set theory; Fuzzy sets; Image analysis; Image classification; Information representation; Marine vehicles; Remote sensing;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
