## Transformations and classifications of remotely sensed data: theory and geological cases |

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### Contents

Orthogonal Transformations of Iultispectral Data | 37 |

Tcxtural Features | 99 |

Linealcnt Intensity Analysis | 133 |

7 other sections not shown

### Common terms and phrases

algorithm Almaden autocorrelation barren bedgroup canonical discriminant functions chapter classified image clustering co—occurrence coefficients computed Confusion matrix Conradsen consider contextual classification correlation corresponding covariance matrices defined described direction distribution Dolerite East Greenland eigenvalues eigenvectors estimate example false color composite fractile frequency Geman geological granites histogram Igaliko intrusive implemented iterations Jeffreys—Matusita distance kernel kurtosis Landsat lineament linear combination linear discriminant analysis logical smoothing Mahalanobis distance maps Markov random field Markovian random fields matrix maximizes maximum likelihood maximum likelihood classification median filter method modus filter MSS7 multivariate neighborhood noise obtained ordinary output parameters pixel pixel values plot postprocessing principal components problem quartzites ratio reject class Remote Sensing scatterogram seen segmentation shown in figure skewness so—called South Greenland spatial squared standard deviation statistical superclasses Table techniques texture theorem training areas training sets transformation unequal covariance matrices variables variance variation vegetation Ymer