Multispectral data classification based on spectral indices and cascaded fuzzy C-mean classifiers

Land Use and Land Cover (LULC) are characterized by a large variety of spectrally distinct LULC classes. The diagnostic and evaluation of the spectral separability measure yields the potential for automated identification and mapping of these classes. This study proposes a new cascaded fuzzy C-mean...

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Bibliographic Details
Published in2009 IEEE International Geoscience and Remote Sensing Symposium Vol. 3; pp. III-821 - III-824
Main Authors Jabloun, M., Mihai, C., Vanhamel, I., Geerinck, T., Sahli, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
base map
Fuzzy C-mean
Image classification
Iris
Layout
Reflectivity
Remote sensing
Robustness
rough classification
Satellites
Soil
Statistics
Vegetation mapping
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Summary:Land Use and Land Cover (LULC) are characterized by a large variety of spectrally distinct LULC classes. The diagnostic and evaluation of the spectral separability measure yields the potential for automated identification and mapping of these classes. This study proposes a new cascaded fuzzy C-mean classification method for a rough classification of LULC classes. The method is based on the use of spectral indices as innovative features to provide a coarse classification of remotely sensed data. The robustness and accuracy of the defined classification schema is performed based on the computation of confusion matrices and Kappa coefficient. The Kappa statistic ranges from 0.90 to 0.98 for the set of the evaluated images which infer the good accuracy of the new rough classification scheme.
ISBN:1424433940
9781424433940
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2009.5417893