257.

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Page 329

Hyperion 11

Hyperspectral data 65, 100

ICM, see Iterated conditional modes

IFOV, see Instantaneous Field of View

IKONOS 11

Image segmentation 246

Incidence angle Fig. 1.13

Inference, fuzzy 165

Input layer 103

Instantaneous Field of View 9, Fig. 1.6

Interferometric radar 34

Inverse Difference Moment, texture 216

Irradiance 12

ISODATA algorithm 69–72, 153

Iterated conditional modes 262–263, 267, 268, 269, 296

Julian Day 20

Kappa coefficient 90, 91, 98

Kappa coefficient, large-sample variance 98–99

Kappa, conditional 99

Knowledge-based methods 81–86

Kohonen’s self-organising feature map, see Self-organising feature map

Kuan filter 43, 49–50

Lambertian reflectance 7, 13, 21, 22, Fig. 1.4(b)

Landsat ETM+ 2, 57, 58

LandsatTM 9, 11, 19, 23

Layover 28–29, Fig. 1.15, 1.16

Learning rate 81, 108

Learning Vector Quantisation algorithm 118

Least squares estimation 257

Lee filter 43, 44–48

Lee sigma filter 48–49

Line process 244–246

Linear mixture model 180

Lipschitz-Holder exponent 204

Logical channel 89

Long-term memory 133

Look angle Fig. 1.13

LVQ algorithm, see Learning Vector Quantisation algorithm

MAF, see Min/max autocorrelation factor

Mahalanobis distance 69, 70, 76, 78–79, 95

Majority filter 88

Majority vote 86, 118

MAP solution, see Maximum a posteriori solution

Mapping cortex 115, 118, Fig. 3.6

Mapping function 104

MAR, see multiplicative autoregressive random field

Markov random field 88, 231, 232–233, 272, 296

Markov random field, parameter estimation 255–260

Markov random field, relation to Gibbs random field 234–237

Markov random field, simplified form 237–239

Markov random field, texture generation 239–241

Markov random field, use in multisource classification 279–280

Maximiser of posterior marginals 262, 266–267, 268, 269

Maximum a posteriori criteria 230, 231

Maximum a posteriori solution 78, 79, 88

Maximum likelihood classifier 55, 58, 67, 76, 77–79, 83, 84, 85, 90, 93, 102, 140, 231, 267, 273, 296

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