1993
DOI: 10.1080/07038992.1993.10855148
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An Empirical Comparison of Evidential Reasoning, Linear Discriminant Analysis, and Maximum Likelihood Algorithms for Alpine Land Cover Classification

Abstract: RESUMELa mise au point de nouvelles methodes d'analyse des donnees de teledetection actuelles etfutures est necessaire si l'on veut surmonter les limites que presentent les methodes classiques de traitement et de classification des images. Dans le present article, l'auteurfait l'eualuation d'un classificateurpar raisonnement d'etndences [Evidential Reasoning] (MERCURY(f)) qui fait appel it la tbeorie des evidences de Dempster-Shafer et qui offre une souplesse accrue ainsi qu 'une nouvellefonctionnalite pour tr… Show more

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Cited by 49 publications
(20 citation statements)
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“…Bennediktsson et al (1990) used Landsat multispectral scanner network imagery and three topographic data sets (elevation, slope and aspect) to classify land cover. Peddle et al (1994) applied the neural network approach to classify land cover in Alpine regions from multi-source remotely sensed data. Gong et al (1996) have tested the feasibility of applying feedforward neural network and backpropagation to land system.…”
Section: Forest Cover Type Predictionmentioning
confidence: 99%
“…Bennediktsson et al (1990) used Landsat multispectral scanner network imagery and three topographic data sets (elevation, slope and aspect) to classify land cover. Peddle et al (1994) applied the neural network approach to classify land cover in Alpine regions from multi-source remotely sensed data. Gong et al (1996) have tested the feasibility of applying feedforward neural network and backpropagation to land system.…”
Section: Forest Cover Type Predictionmentioning
confidence: 99%
“…The occurrence and seasonal flowering phases of these species were used to map zones that present pollen allergenic risk. First, high-resolution satellite remote sensing images were pre-processed to extract vegetation cover for the study area [23,24]. Tree types are judged by the information of vegetation spectra and texture-because the different tree species have different canopy shape features, different leaf size and density, and the canopy images reflected in the remote sensing images have different texture features.…”
Section: Study Methodsmentioning
confidence: 99%
“…Probabilistic techniques such as the maximum likelihood classifier and discriminant analysis have been particularly popular. These approaches have firm statistical foundations and allocate each case (e.g ., pixel) to the class with which it has the highest probability of membership (Peddle, 1993;Mather 1999a). Although this is an intuitively appealing approach and can be accurate, the correct application of such classifications requires the satisfaction of several assumptions that are not always tenable (Section 2.4.3) and it is sometimes difficult to integrate ancillary data into the analysis.…”
Section: Supervised Classificationmentioning
confidence: 99%