TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

More documents

Recommendations

Info

Acknowledgments I would like to acknowledge all the people who have contributed to my human and professional growth during these three years. I express my gratitude to Prof. Fabio Del Frate, for his guidance, for his advices and the encouragement to join the Earth Observation Laboratory in Tor Vergata. I thank Prof. Domenico Solimini for his valuable support provided during all these years with suggestions and comments and I wish to thank Prof. Schiavon for his help in a stage of my PhD path. I am very grateful to Sergio D‘Elia and the KEO team, Michele Iapaolo and Andrea Della Vecchia, for allowed me to support the activities of the Research and Service Support Section at ESA and for their precious comments during my internship. I would like to thank my friends and colleagues that I met during these years in the Geoinformation laboratory of Tor Vergata: Daniele Archilei, Raja Chakraborty, Federico Di Giuseppe, Antonio Di Noia, Irene Fabbrini, Daniele Latini, Gaia Laurin, Simona Manailescu, Matteo Picchiani, Fabio Pacifici, Barbara Polsinelli, Andrea Radius and Stefano Robustelli. I also thank Chiara Pratola for share several moments as young researches and a special thanks to Emanuele Angiuli and Rachid Rahmoune, loyal friends more than only colleagues. I wish to thank all the people and friends from ESA: Antonio Biscuso, Alessandro Burini, Cosimo Putignano, Riccardo Duca, Vasilis Kalougirou, Marco Lavalle, Andrea Minchella and Chiara Solimini. A special thanks to my friends from my
Page 1: TOR VERGATA UNIVERSITY Department o
Page 5 and 6: Table of Contents Abstract ________
Page 7 and 8: 5. Spectral, textural and shape cha
Page 9 and 10: Abstract Abstract 1 Today the conti
Page 11 and 12: Introduction Introduction 3 The ret
Page 13 and 14: Introduction 5 Since the late ‗70
Page 15 and 16: (a) (b) (c) Introduction 7 Figure 3
Page 17 and 18: Introduction 9 The obtained classif
Page 19 and 20: Chapter 1 Automatic classification
Page 21 and 22: Chapter 1 13 response of a specific
Page 23 and 24: 1.2 Classification methods Chapter
Page 25 and 26: Chapter 1 17 they begin to be used
Page 27 and 28: Chapter 1 19 This interaction deter
Page 29 and 30: d) Training Cycles Chapter 1 21 The
Page 31 and 32: - Chapter 1 23 - Neuron outside the
Page 33 and 34: 1.4 Supervised methods Chapter 1 25
Page 35 and 36: 1.5.1 Texture Chapter 1 27 Texture
Page 37 and 38: Chapter 1 29 where i, j are the gra
Page 39 and 40: 1.5.2.1 Watershed transformation Ch
Page 41 and 42: Chapter 2 Used dataset Chapter 2 33
Page 43 and 44: Quickbird specifications Chapter 2
Page 45 and 46: 2.1.2 Quickbird image 2: Tor Vergat
Page 47 and 48: 2.1.3 Quickbird image 3: Denver (CO
Page 49 and 50: 2.2 Hyperspectral sensors Chapter 2
Page 51 and 52: AHS flight specifications Date June
Page 53 and 54:
2.2.2 MIVIS Chapter 2 45 MIVIS (Mul
Page 55 and 56:
Chapter 2 47 Figure 2.8: MIVIS imag
Page 57 and 58:
Chapter 3 Methodology Chapter 3 49
Page 59 and 60:
Chapter 3 51 that the nodes of the
Page 61 and 62:
Chapter 3 53 Following these consid
Page 63 and 64:
Chapter 3 55 Fig. 3.2 shows the gen
Page 65 and 66:
3.3 The pixel context: texture Chap
Page 67 and 68:
3.4 The pixel context: segmentation
Page 69 and 70:
Chapter 3 61 where Oi and Oj are th
Page 71 and 72:
Fig. 3.3: unsupervised clustering a
Page 73 and 74:
Chapter 4 Pre-processing Chapter 4
Page 75 and 76:
Chapter 4 67 For this work, it has
Page 77 and 78:
Fig. 4.1: Component of apparent ref
Page 79 and 80:
4.1.2 Atmospheric correction of the
Page 81 and 82:
Chapter 4 73 All the parameters fro
Page 83 and 84:
Chapter 4 75 Use of the DISORT mult
Page 85 and 86:
Radiance µW/(cm^2*nm*sr) Reflectan
Page 87 and 88:
4.2 Building shadows removal Chapte
Page 89 and 90:
The solar radiation balance on the
Page 91 and 92:
4.2.2 Application of Simulated refl
Page 93 and 94:
4.2.3 Results Chapter 4 85 As shown
Page 95 and 96:
Chapter 4 87 Fig. 4.7: Details of A
Page 97 and 98:
Chapter 4 89 This section can be co
Page 99 and 100:
Chapter 5 Chapter 5 91 Spectral, te
Page 101 and 102:
Reflectance 0,6 0,5 0,4 0,3 0,2 0,1
Page 103 and 104:
Reflectance 0,1 0,09 0,08 0,07 0,06
Page 105 and 106:
Reflectance 0,4 0,35 0,3 0,25 0,2 0
Page 107 and 108:
Chapter 5 99 Fig. 5.4: buildings ra
Page 109 and 110:
Reflectance Chapter 5 101 Fig. 5.6:
Page 111 and 112:
Chapter 5 103 For these reasons, it
Page 113 and 114:
Chapter 6 Chapter 6 105 Pixel based
Page 115 and 116:
Chapter 6 107 the following cluster
Page 117 and 118:
Chapter 6 109 Fig. 6.2: Nettuno fal
Page 119 and 120:
Page 121 and 122:
Chapter 6 113 Fig. 6.5: Bari false
Page 123 and 124:
Page 125 and 126:
Chapter 6 117 Fig. 6.8: Tor Vergata
Page 127 and 128:
Chapter 6 119 Fig. 6.11: Tor Vergat
Page 129 and 130:
Chapter 6 121 From the interactive
Page 131 and 132:
Chapter 6 123 In other cases, if th
Page 133 and 134:
6.2.2 Roads class Chapter 6 125 The
Page 135 and 136:
Chapter 6 127 Fig. 6.17: Nettuno fa
Page 137 and 138:
Chapter 6 129 Fig. 6.19: Tor Vergat
Page 139 and 140:
Chapter 6 131 level of class discri
Page 141 and 142:
6.4.1 Nettuno (I) Chapter 6 133 For
Page 143 and 144:
Fig. 6.21: Nettuno false color comp
Page 145 and 146:
Overall accuracy 88.125 % (SOM+MLP)
Page 147 and 148:
6.4.3 Denver (US) Chapter 6 139 The
Page 149 and 150:
Fig. 6.23: Denver false color compo
Page 151 and 152:
Overall accuracy 92.15 % (SOM + MLP
Page 153 and 154:
Chapter 6 145 Fig. 6.24: Bari false
Page 155 and 156:
Overall accuracy 89.62% (MLP + SOM)
Page 157 and 158:
Chapter 7 Chapter 7 149 Application
Page 159 and 160:
Chapter 7 151 speed) into urban met
Page 161 and 162:
Figure 7.1: role of classification
Page 163 and 164:
Chapter 7 155 The optimal NN archit
Page 165 and 166:
Chapter 7 157 Fig 7.4: details of R
Page 167 and 168:
Chapter 7 159 automatism. In next p
Page 169 and 170:
Chapter 7 161 where εv and εs are
Page 171 and 172:
Chapter 7 163 correlations consider
Page 173 and 174:
Chapter 7 165 Figure 7.5: Validatio
Page 175 and 176:
Chapter 7 167
Page 177 and 178:
Conclusions 169 The features to ext
Page 179 and 180:
Conclusions 171 years, the develope
Page 181 and 182:
Bibliography Bibliography 173 [1] J
Page 183 and 184:
Bibliography 175 [19] A.K. Shackelf
Page 185 and 186:
Bibliography 177 Resolution Optical
Page 187 and 188:
Bibliography 179 [131] L. Bruzzone
Page 189 and 190:
Bibliography 181 [150] D. Chen, D.
Page 191 and 192:
Bibliography 183 [205] F. Del Frate
Page 193 and 194:
Bibliography 185 [316] D. H. Dougla
Page 195 and 196:
Bibliography 187 [414] Y. J. Kaufma
Page 197 and 198:
Bibliography 189 [508] M. Lazzarini
Page 199 and 200:
Bibliography 191 [717] A. Berk, G.
Page 201 and 202:
List of acronyms and abbreviations
Page 203 and 204:
Curriculum Vitae CV, conferences an
Page 205:
Publications CV, conferences and pu
show all

TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

Create successful ePaper yourself

Delete template?

Save as template?