Transformations and Classifications of Remotely Sensed Data: Theory and Geological Cases |
Contents
Linear and Quadratic Discrimination | 147 |
Feature Selection in Discriminant Analysis | 177 |
Contextual classification | 205 |
5 other sections not shown
Common terms and phrases
algorithm Almaden autocorrelation barren bedgroup behandles centerpoint chapter clustering coefficients computed Conradsen consider contextual classification correlation corresponding covariance matrices defined described discriminant analysis distribution eigenvalues eigenvectors estimate example F-test false color composite Feature selection feature vector Fortran fractile frequency function Geman geological granites hierarchical Igaliko intrusive Igaliko scene image data Image Processing Group implemented IMSOR iterations Jeffreys-Matusita distance kapitel kernel km² kurtosis Landsat lineament linear combination linear discriminant analysis Mahalanobis distance maps Markovian random fields matrix maximum likelihood mean median filter methods Migmatite neighborhood Nielsen ordinary output Pascal-like code pixel plot Postprocessing principal components Related routines Remote Sensing remotely sensed data result scatterogram sdev seen segmentation shown in figure skewness South Greenland spatial spectral squares standard deviation statistical stepwise Table techniques teknikker theorem thesis Thyrsted training areas training sets transformations unequal covariance matrices uranium variables variation vegetation Ymer