348.

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Page vi

		3.6  Neural networks in remote sensing image classification		140
4		Methods based on fuzzy set theory		149
		4.1  Introduction to fuzzy set theory		150
		4.2  Fuzzy c-means clustering algorithm		153
		4.3  Fuzzy maximum likelihood classification		157
		4.4  Fuzzy rule base		159
		4.5  Image classification using fuzzy rules		169
		4.6  Fuzzy classification: interpretation of mixed pixels		176
5		Texture quantisation		186
		5.1  Fractal and multifractal dimensions		187
		5.2  Frequency domain filtering		207
		5.3  Grey level co-occurrence matrix (GLCM)		212
		5.4  Multiplicative autoregressive random fields		216
		5.5  The semivariogram and window size determination		219
		5.6  Experimental analysis		223
6		Modelling context using Markov random fields		230
		6.1  Markov random fields and Gibbs random fields		231
		6.2  Construction of posterior energy		241
		6.3  Robust M estimator		251
		6.4  Parameter estimation		255
		6.5  MAP-MRF classification algorithms		260
		6.6  Experimental results		267
7		Multisource classification		271
		7.1  Stacked-vector method		272
		7.2  Incorporating topographic data		273
		7.3  The extension of Bayesian classification theory		274
		7.4  Evidential reasoning		281
		7.5  Dealing with source reliability		289
		7.6  Experimental results		295
		References		299
		Index		326

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