TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

More documents

Recommendations

Info

34 Used dataset 2.1 QuickBird QuickBird (Fig. 2.1) was the first satellite in a constellation of spacecraft that Digital Globe is still developing for commercial high-resolution imagery of Earth [201]. QuickBird‘s global collection of panchromatic and multispectral imagery is designed to support applications ranging from map publishing to land and asset management to insurance risk assessment. Figure 2.1: quickbird satellite The QuickBird satellite provides images consisting of four multi-spectral (MS) channels with 2.4 m resolution and a single panchromatic (PAN) band with a 0.62 m resolution (Table 2.1). The four MS bands collect data at red, green, blue, and near-infrared wavelengths, and the data in each band is stored with an 11-bit quantization. As previously discussed, the spatial resolution plays a key role in urban monitoring related to the ability to detect fine-scale objects present in urban scenes.
Quickbird specifications Chapter 2 35 Launch date October 18, 2001 Altitude 450 km, 98 degree, sun-synchronous inclination Swath Areas of interest. - Single Area: 16.5 km x 16.5 km - Strip: 16.5 km x 115 km Spectral resolution - Blue: 450 - 520 nanometers - Green: 520 - 600 nanometers - Red: 630 - 690 nanometers - Near-IR: 760 - 900 nanometers - PAN: 445 - 900 nanometers Spatial resolution - Multi: 244 centimeter at nadir - PAN: 62 centimeter at nadir Revisiting time 2-3 days, depending on latitude Table 2.1: Quickbird satellite specifications 2.1.1 Quickbird image 1: Nettuno (Rome, Italy) The first analysed image has been acquired above Nettuno area -Rome, Italy- (Fig 2.3, Table 2.2). This area was chosen for the presence of all the five main cover classes to spot. Being Nettuno a coastal urban area, it has been decide to use the automatic classification procedure to highlight water, vegetation, bare soil, buildings and roads. Image characteristics Image name Nettuno (Italy) Satellite Quickbird Date: July, 22 nd 2003 N° rows 5383 N° columns 4846 N° pixels 26086018 Table 2.2: Quickbird image Nettuno specifications
Page 1: TOR VERGATA UNIVERSITY Department o
Page 4 and 5: ―Office‖ in ESRIN: Annekatrien
Page 6 and 7: 2. Used dataset ___________________
Page 8 and 9: 7. Application of automatic classif
Page 10 and 11: 2 Abstract of the VHR imagery with
Page 12 and 13: 4 The retrieval of information in u
Page 18 and 19: 10 The retrieval of information in
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 44 and 45: 36 Used dataset Figure 2.2: A QB im
Page 50 and 51: 42 Used dataset Figure 2.5: AHS sys
Page 52 and 53: 44 Used dataset Figure 2.6: AHS ima
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
Page 60 and 61: 52 Methodology Fig.3.1: MIVIS RGB 2
Page 62 and 63: 54 Methodology 3.2 The mixed approa
Page 64 and 65: 56 Methodology Fig 3.2: example of
Page 66 and 67: 58 Methodology results and it was p
Page 68 and 69: 60 Methodology where quant_levels r
Page 70 and 71: 62 Methodology starting one from th
Page 72 and 73: 64 Methodology
Page 74 and 75: 66 Pre-processing 4.1 Atmospheric c
Page 76 and 77: 68 Pre-processing 4.1.1 The FLAASH-
Page 78 and 79: 70 Pre-processing 4.1.1.1. Atmosphe
Page 80 and 81: 72 Pre-processing 4.1.2.2 Input par
Page 82 and 83: 74 Pre-processing With the option a
Page 84 and 85: 76 Pre-processing 4.1.3 Atmospheric
Page 86 and 87: 78 Pre-processing The signatures im
Page 88 and 89: 80 Pre-processing 4.2.1 Simulated r
Page 90 and 91: 82 Pre-processing where the first t
Page 92 and 93:
84 Pre-processing Fig. 4.5: solar r
Page 94 and 95:
86 Pre-processing Fig. 4.6: AHS fal
Page 96 and 97:
88 Pre-processing Fig 4.8: AHS real
Page 98 and 99:
90 Pre-processing
Page 100 and 101:
92 5.1 Vegetation Spectral, textura
Page 102 and 103:
94 5.2 Water Spectral, textural and
Page 104 and 105:
96 5.3 Bare Soil Spectral, textural
Page 106 and 107:
98 5.4 Buildings Spectral, textural
Page 108 and 109:
100 5.5 Roads Spectral, textural an
Page 110 and 111:
102 Spectral, textural and shape ch
Page 112 and 113:
104 Spectral, textural and shape ch
Page 114 and 115:
106 Pixel based classification of V
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
118 Reflectance Reflectance 0,45 0,
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
128 Reflectance Pixel based classif
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
142 6.4.4 Bari (I) Pixel based clas
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
150 Application of automatic classi
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
168 Conclusions Conclusions In this
Page 178 and 179:
170 Conclusions In fact, the traini
Page 180 and 181:
172 Conclusions (i.e. topological r
Page 182 and 183:
174 Bibliography [10] P. M. Teillet
Page 184 and 185:
176 Bibliography [102] Geoeye, Ikon
Page 186 and 187:
178 Bibliography [122] P. Dreyer,
Page 188 and 189:
180 Bibliography [141] P. Gong, D.
Page 190 and 191:
182 Bibliography [160] B. Guindon,
Page 192 and 193:
184 Bibliography [307] F. Melgadi a
Page 194 and 195:
186 Bibliography [407] Z. Qu, B. Ki
Page 196 and 197:
188 Bibliography [424] C. Elachi,
Page 198 and 199:
190 Bibliography [709] A. R. Gilles
Page 200 and 201:
192 Bibliography
Page 202 and 203:
194 List of acronyms and abbreviati
Page 204 and 205:
196 CV, conferences and publication
show all

TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

Create successful ePaper yourself

Delete template?

Save as template?