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The Tasseled Cap de-mystified

Eric P. Crist, Richard J. Kauth

Open publisher page 295 citations

Abstract

The fundamental concepts on which the Tasseled Cap transformations of MSS and TM data are based - particularly the identification of inherent data structures - are explained and discussed. Emphasis on the structures present in data from any given sensor, which are themselves the expression of physical characteristics of scene classes, provides a number of advantages, including (a) reduction in data volume with minimal information loss; (b) spectral features which can be applied, without re-definition or adjustment, to any data set for a given sensor; (c) spectral features which can be directly associated with important physical parameters; and (d) easier integration of data from multiple sensors.

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What this paper is about

The fundamental concepts on which the Tasseled Cap transformations of MSS and TM data are based - particularly the identification of inherent data structures - are explained and discussed. Emphasis on the structures present in data from any given sensor, which are themselves the expression of physical characteristics of scene classes, provides a number of advantages, including (a) reduction in data volume with minimal information loss; (b) spectral features which can be applied, without re-definition or adjustment, to any data set for a given sensor; (c) spectral features which can be directly associated with important physical parameters; and (d) easier integration of data from multiple sensors.

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OpenAlex reports 295 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The fundamental concepts on which the Tasseled Cap transformations of MSS and TM data are based - particularly the identification of inherent data structures - are explained and discussed. Emphasis on the structures present in data from any given sensor, which are themselves the expression of physical characteristics of scene classes, provides a number of advantages, including (a) reduction in data volume with minimal information loss; (b) spectral features which can be applied, without re-definition or adjustment, to any data set for a given sensor; (c) spectral features which can be directly associated with important physical parameters; and (d) easier integration of data from multiple sensors.

Key concepts: Identification (biology), Set (abstract data type), Volume (thermodynamics), Data set, Reduction (mathematics), Computer science, Data mining, Physics

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