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A comparison of canonical discriminant analysis and principal component analysis for spectral transformation

A comparison of canonical discriminant analysis and principal component analysis for spectral transformation

PEandRS, Photogrammetric Engineering and Remote Sensing 66(7): 841-847

A study was conducted in the Upper Peninsula, Michigan, USA, to test the strength and weakness of canonical discriminant analysis (CDA) as a spectral transformation technique to separate ground scene classes which have close spectral signatures. Landsat Thematic Mapper images dated August and September 1991, covering forest land. Classification accuracies using CDA transformed images were compared to those using principal component analysis (PCA) transformed images.

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