TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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10 The retrieval of information in urban areas: a challenging issue
Chapter 1 Automatic classification of Very High Chapter 1 11 Resolution optical images: state of the art This chapter aims at describing the recent progress in the classification of urban areas, discussing the evolution from the first multispectral sensor from ‗70s until the recent Very High Resolution multispectral and hyperspectral satellites. The advantages derived by an improved spatial resolution opened new scenarios in the amount of information to retrieve from urban areas, with a detail of a narrow road and a single building. New image information mining techniques have been developed in these last years and appreciable results derived from the proposed methodology based on neural networks classification methods.
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Page 4 and 5: ―Office‖ in ESRIN: Annekatrien
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Page 10 and 11: 2 Abstract of the VHR imagery with
Page 12 and 13: 4 The retrieval of information in u
Page 20 and 21: 12 Automatic classification of Very
Page 30 and 31: 22 Where: Automatic classification
Page 36 and 37: 28 1.5.1.1 GLCM Automatic classific
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Page 54 and 55: 46 Used dataset The image belongs t
Page 56 and 57: 48 Used dataset
Page 58 and 59: 50 Methodology 3.1 First tests and
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Page 62 and 63: 54 Methodology 3.2 The mixed approa
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Page 66 and 67: 58 Methodology results and it was p
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60 Methodology where quant_levels r
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62 Methodology starting one from th
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64 Methodology
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66 Pre-processing 4.1 Atmospheric c
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68 Pre-processing 4.1.1 The FLAASH-
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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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