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- W2559967609 abstract "This work joins the assets of two different classification procedures for National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data. The first procedure presented by Rodriguez Yi et al. (2000) was based on image segmentation following supervised classification by regions. Eight vegetation classes were identified using this procedure. A Kappa coefficient of 0.4 indicated that image segmentation associated to supervised classification by regions is a procedure that is useful for mapping vegetation classes on a regional scale. SPRING software was used to perform image segmentation and supervised classification using AVHRR channel 1 and 2 mosaics. Prior to image segmentation (region growing algorithm), the histograms of these channels were equalized to avoid a preference for a channel with large variance. The best segmentation treshold values for area and similarity were 2 and 25, respectively. Supervised classification by regions was based on the Bhattacharrya distance with a threshold of 95% for correct classification. Twelve Landsat images together with field information were used as ancillary data to support the training sample selection in the supervised classification procedure. The second procedure presented by Durieux et al. (2000) was based on fuzzy logic classification of multisource data and NOAA-AVHRR images. This methodology used jointly overlay operations, multiple criteria analysis methods and fuzzy procedure. Vegetation classification was based on the biogeographical analysis of relationships between geographical data and vegetation distribution associated to remote sensing information given by the spectral response of each vegetation type. Multi-source data were associated to expert knowledge using a fuzzy ponderation. Resulting maps described the potentiality of each vegetation class to be present in a pixel in relation with the considered criterion. Fusions of possibility distribution for each vegetation class were done using a new individualized method. Finally a maximum operator was used to discriminate vegetation class potentialities in the final integration. The main characteristics of this method were the use of possibility theory to handle imprecision due to pixel classification, and the ability to merge numerical sources (satellite image spectral bands, climatic map, DEM, soil map) and symbolic sources (expert knowledge about best localization of classes)." @default.
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- W2559967609 date "2000-01-01" @default.
- W2559967609 modified "2023-09-27" @default.
- W2559967609 title "FUZZY CLASSIFICATION BY REGION OF SEGMENTED NOAA- AVHRR IMAGES AND MULTISOURCE DATA" @default.
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