TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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―Office‖ in ESRIN: Annekatrien Debien, Christian Kogler, Anastasia Polychronaki and Young Koo Soh. I would also especially like to thank my mum for her continuous support, encouragement and patience. I wish also to thank my father, my close relatives and ―in-laws‖ relatives from Rome for their support during these years. To conclude, I acknowledge the person who changed my life, giving me so much love and confidence to reach my goals. Without you, my Vale, I would have never made it.
Table of Contents Abstract ________________________________________________ 1 Introduction ____________________________________________ 3 1. Automatic Classification of Optical Very High Resolution Images: state of the art ________________________________ 11 1.1 The evolution of optical sensors .................................................................. 12 1.2 Classification methods ................................................................................ 15 1.3 Unsupervised methods ................................................................................. 15 1.3.1 SOM..................................................................................................... 17 1.3.1.1 Net structure ...................................................................................... 17 1.3.1.2 SOM Parameters Setting ...................................................................... 20 1.3.1.3 SOM Learning Process ........................................................................ 23 1.4 Supervised methods ..................................................................................... 25 1.4.1 Neural Networks .................................................................................. 25 1.5 The importance of spatial parameters in VHR images: texture and shape analysis ............................................................................................ 26 1.5.1 Texture analysis .................................................................................. 27 1.5.1.1 GLCM .............................................................................................. 28 1.5.2 Shape analysis: segmentation ............................................................. 29 1.5.2.1 Watershed transformation ..................................................................... 31
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Page 6 and 7: 2. Used dataset ___________________
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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
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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
Page 42 and 43: 34 Used dataset 2.1 QuickBird Quick
Page 44 and 45: 36 Used dataset Figure 2.2: A QB im
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46 Used dataset The image belongs t
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48 Used dataset
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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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