TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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20 Automatic classification of Very High Resolution images: state of the art 1.3.1.2 SOM Parameters Setting Different parameters must be defined in order to synthesize a SOM. The parameters are: dimension, learning rate, neighborhood function and training cycles. In the following subsection a description of these parameters is described. a) Dimension A bi-dimensional configuration is usually preferred. Three-dimensional configuration does not give a substantial advantage during the elaboration of information and usually causes a complication in the management of the map itself. The number of neurons is variable and it depends on the purposes of the analysis, the input structure and the internal relationships between neurons. b) Learning Rate In the early cycles, value should be 1 and then decreased until it reaches its minimum value. A possible formulation is: where ηmin=0, ηmax= 1, tmax = number of cycles and t is the current cycle. c) Neighborhood Function This parameter defines the area of the bubble: if it is too small, the bubble does not contains an enough number of neurons inside it and the network could not be ordered after the training phase. It is possible to avoid this problem by assigning to that variable a value greater than the half of the diameter of the network. During the training cycle, it can be reduced until 0.5, which means that only the winning neuron has a modification of its weight.
d) Training Cycles Chapter 1 21 The training process is a stochastic process, so the number of cycles depends on the specific case: if it is too small, the result does not converge; on the other hand, too high value tends to incorporate many classes in a unique one In figure 1.3, it is possible to see a Kohonen‘s map with two dimensions where neuron ηkj is connected with its neighborhood. i Fig. 1.3: Schematic representation of Kohonen SOM When an input from the layer w activates a neuron in the η layer, the interaction that the winning neuron (selected with a user-defined rule) establishes with its neighbors is defined according to a neighborhood function, which normally takes value between 0 and 1. This interaction determines a modification of the weights dependent on the neighborhood function and the response of the neuron. The ηj neuron activity is defined by:
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70 Pre-processing 4.1.1.1. Atmosphe
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72 Pre-processing 4.1.2.2 Input par
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74 Pre-processing With the option a
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76 Pre-processing 4.1.3 Atmospheric
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78 Pre-processing The signatures im
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80 Pre-processing 4.2.1 Simulated r
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82 Pre-processing where the first t
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84 Pre-processing Fig. 4.5: solar r
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86 Pre-processing Fig. 4.6: AHS fal
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88 Pre-processing Fig 4.8: AHS real
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90 Pre-processing
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92 5.1 Vegetation Spectral, textura
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94 5.2 Water Spectral, textural and
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96 5.3 Bare Soil Spectral, textural
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98 5.4 Buildings Spectral, textural
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100 5.5 Roads Spectral, textural an
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102 Spectral, textural and shape ch
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104 Spectral, textural and shape ch
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106 Pixel based classification of V
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118 Reflectance Reflectance 0,45 0,
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128 Reflectance Pixel based classif
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142 6.4.4 Bari (I) Pixel based clas
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150 Application of automatic classi
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168 Conclusions Conclusions In this
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170 Conclusions In fact, the traini
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172 Conclusions (i.e. topological r
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174 Bibliography [10] P. M. Teillet
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176 Bibliography [102] Geoeye, Ikon
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178 Bibliography [122] P. Dreyer,
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180 Bibliography [141] P. Gong, D.
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182 Bibliography [160] B. Guindon,
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184 Bibliography [307] F. Melgadi a
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186 Bibliography [407] Z. Qu, B. Ki
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188 Bibliography [424] C. Elachi,
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190 Bibliography [709] A. R. Gilles
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192 Bibliography
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194 List of acronyms and abbreviati
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196 CV, conferences and publication
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