TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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6 The retrieval of information in urban areas: a challenging issue Atmospheric correction provides estimates of the radiation emitted and reflected at the surface, and it is necessary for observing parameters that are intrinsic to the surface. accurate atmospheric correction removes the effects of changes in satellite- sun geometry and atmospheric conditions due to aerosol scattering [10]. Atmospherically corrected surface reflectance images improve the accuracy of surface type classification [11], [12] and allow to compare the spectral signatures from different images. Figure 2: a detail of roads and buildings from Quickbird images (Tor Vergata area) Therefore, the use of other information on top of spectral information for the urban land-cover classification is essential [13], [14]. This additional information can be the features used in visual interpretation, such as texture and shape (Fig. 3), transposed to digital analysis. The features used in these interpretation keys are: shape, size, pattern, tone, texture, shadows, site and association [15], [16], [17]. This additional information may help to overcome spectral similarities between specific classes [18], [19], [20].
(a) (b) (c) Introduction 7 Figure 3: visualization of Quickbird image of Tor Vergata with spectral (a), texture (b) and shape (c) information
Page 1: TOR VERGATA UNIVERSITY Department o
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56 Methodology Fig 3.2: example of
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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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TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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