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Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels
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1997
Year
EngineeringLand UseLinear Mixture ModellingFuzzy C-means ClassifierLand CoverSocial SciencesImage ClassificationImage AnalysisData SciencePattern RecognitionMlc ProbabilitiesMaximum Likelihood ClassificationEdge DetectionFuzzy Pattern RecognitionFuzzy C-means ClassificationFuzzy LogicMachine VisionSoil ClassificationGeographyComputer EngineeringComputer ScienceMedical Image ComputingSignal ProcessingComputer VisionLand Cover MapLinear Mixture ModelRemote SensingCover MappingTexture AnalysisFuzzy ClusteringImage Segmentation
Abstract Abstract Three different 'soft' classifiers (fuzzy c-means classifier, linear mixture model, and probability values from a maximum likelihood classification) were used for unmixing of coarse pixel signatures to identify four land cover classes (i.e., supervised classifications). The coarse images were generated from a 30m Thematic Mapper (TM) image; one set by mean filtering, and another using an asymmetric filter kernel to simulate Multi-Spectral Scanner (MSS) sensor sampling. These filters collapsed together windows of up to 11 11 pixels. The fractional maps generated by the three classifiers were compared to truth maps at the corresponding scales, and to the results of a hard maximum likelihood classification. Overall, the fuzzy c-means classifier gave the best predictions of sub-pixel landcover areas, followed by the linear mixture model. The probabilities differed little from the hard classification, suggesting that the clusters should be modelled more loosely. This paper demonstrates successful methods for use and comparison of the classifiers that should ideally be extended to a real dataset.