TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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7. Application of automatic classification in urban thermography __________________________________ 149 7.1 Urban Heat Island and Land Surface Temperature .................................. 150 7.2 How classification techniques can help urban thermography .................. 152 7.3 Building extraction .................................................................................... 153 7.4 LST retrieval .............................................................................................. 158 7.4.1. Emissivity ......................................................................................... 160 7.4.2. Water Vapour ................................................................................... 162 7.5 Final results ............................................................................................... 164 Conclusions __________________________________________ 167 Bibliography __________________________________________ 173 List of acronyms _______________________________________ 193 CV, conferences and publications _________________________ 195 CV .................................................................................................................... 195 Conferences ..................................................................................................... 196 Publications ..................................................................................................... 197
Abstract Abstract 1 Today the continuous increase in archives' size and EO (Earth Observation) sensors' variety requires new methodologies and tools for information mining and management. With this growth, new approaches for image information mining, multi-domain information management, and knowledge management and sharing (in support of information mining) are necessary. An automatic chain reduces the human assistance in the data analysis, allows to gain time on the entire image processing and can be designed to retrieve information in Near Real Time (NRT). Such concepts find an important field of application in the monitoring of urban environments, where the knowledge of changes in either natural resources or man- made structures is a valuable source of information for decision-making processes. These images opened new scenario in classification, considering the information deriving from the study of the context and pattern recognition in addition to the spectral characteristics. This information can be the used in visual interpretation, such as texture and shape, transposed to digital analysis. Besides, the availability of hyperspectral data with a spatial resolution around few meters joins the potentiality
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50 Methodology 3.1 First tests and
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52 Methodology Fig.3.1: MIVIS RGB 2
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54 Methodology 3.2 The mixed approa
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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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