259.

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Page 330

Maximum noise fraction 59, 66–67, 100, 182

Mean filter 41–42, Fig. 1.25

Mean square error criterion 105

Mean vector 58

Median filter 41–42, Fig. 1.25

Membership function 150, 158, 160, 169

Membership function, fuzzy 160–164

Membership grade 150

Metropolis algorithm 240

Migrating means 68

Min/max autocorrelation factor 59, 65–66, 100

Minimum distance classifier 55, 76

Minnaert constant, 23

Minnaert model, 21–24

Mixed pixel 176

ML classifier, see Maximum likelihood classifier

MLL, see Multilevel logistic

MNF, see Maximum noise fraction

Momentum term 81, 106, 108

MPM, see Maximiser of posterior marginals

MRF, see Markov Random Field

Multifractal dimension 94, 202–205, 224

Multifractal dimension, estimation of 205–207

Multifractal 187

Multilayer perceptron 102, 103, 122, 140, 141

Multilevel logistic model 238, 239, 240, 242

Multi-look processing 40

Multiple classifier approach 87

Multiplicative autoregressive random field 187, 216–219, 221, 226

Multiplicative autoregressive random field, definition 216–218

Multiplicative autoregressive random field, estimation 218–219

Multisource classification, examples 295–297

Multisource consensus 276

Mutation 293

NDVI, see Normalised Difference Vegetation Index

Neighbourhood 232

Network pruning 108, 114

Network weights, initial values 106–107, 109

Network weights, updating 106

Neurone 79, 102, 103

Neurone, activity 79

Noise-adjusted principal components 66

Non-linear mapping 121

Non-parametric 102

Normalised Difference Vegetation Index 8, 9

Offset 19

Optical depth 16

Optimal brain damage 114

Optimum brain surgeon 114

Orthogonal transform 58

Output layer 103

Overall accuracy 95

Over-fitting 108

Over-training 113–114, Fig. 3.5

Parallelepiped classifier 75–76

Path radiance 12, 13, Fig. 1.7

Pattern recognition 1

Pattern 54

PCA, see Principal components analysis

Perceptron 102

Phase 34

Plausibility 284

Polarimetric radar 33–40

Polarisation signature 39–40, Fig. 1.23

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