Classification Methods for Remotely Sensed Data

Classification Methods for Remotely Sensed Data

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 332 p.
  • 言語 ENG,ENG
  • 商品コード 9780415259095
  • DDC分類 621.3678

Table of Contents

Preface                                            vii
Remote sensing in the optical and microwave 1 (53)
regions
Introduction to remote sensing 5 (6)
Optical remote sensing systems 11 (1)
Atmospheric correction 12 (9)
Correction for topographic effects 21 (3)
Remote sensing in the microwave region 24 (1)
Radar fundamentals 25 (8)
Imaging radar polarimetry 33 (7)
Radar speckle suppression 40 (14)
Pattern recognition principles 54 (48)
Feature space manipulation 56 (11)
Feature selection 67 (1)
A brief description of pattern recognition 68 (18)
techniques
Combining classifiers 86 (1)
Incorporation of ancillary information 87 (3)
Sampling scheme and sample size 90 (5)
Estimation of classification accuracy 95 (5)
Epilogue 100(2)
Pattern recognition using artificial neural 102(47)
networks
Multi-layer perceptron 103(11)
Kohonen's self-organising feature map 114(8)
Counter-propagation networks 122(3)
Hopfield networks 125(8)
Adaptive resonance theory (ART) 133(7)
Neural networks in remote sensing image 140(9)
classification
Methods based on fuzzy set theory 149(37)
Introduction to fuzzy set theory 150(3)
Fuzzy c-means clustering algorithm 153(4)
Fuzzy maximum likelihood classification 157(2)
Fuzzy rule base 159(10)
Image classification using fuzzy rules 169(7)
Fuzzy classification: interpretation of 176(10)
mixed pixels
Texture quantisation 186(44)
Fractal and multifractal dimensions 187(20)
Frequency domain filtering 207(5)
Grey level co-occurrence matrix (GLCM) 212(4)
Multiplicative autoregressive random fields 216(3)
The semivariogram and window size 219(4)
determination
Experimental analysis 223(7)
Modelling context using Markov random fields 230(41)
Markov random fields and Gibbs random fields 231(10)
Construction of posterior energy 241(10)
Robust M estimator 251(4)
Parameter estimation 255(5)
MAP-MRF classification algorithms 260(7)
Experimental results 267(4)
Multisource classification 271(28)
Stacked-vector method 272(1)
Incorporating topographic data 273(1)
The extension of Bayesian classification 274(7)
theory
Evidential reasoning 281(8)
Dealing with source reliability 289(6)
Experimental results 295(4)
References 299(27)
Index 326