TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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32 Automatic classification of Very High Resolution images: state of the art
Chapter 2 Used dataset Chapter 2 33 Considering the improvement of spatial and spectral resolution during these years for VHR images, it has been chosen to design the entire process on Quickbird images (very high spatial resolution and multispectral sensor) and airborne hyperspectral images (very high spatial resolution and hyperspectral sensor). In this chapter, the technical specifications of Quickbird satellite, Airborne Hyperspectral Scanner (AHS) and Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) will be provided, as well as the acquired image details.
Page 1: TOR VERGATA UNIVERSITY Department o
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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 42 and 43: 34 Used dataset 2.1 QuickBird Quick
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
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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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