TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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40 Used dataset Figure 2.4: A QB image taken over Denver (Colorado, USA), on March 10, 2007 (courtesy of Digital Globe)
2.2 Hyperspectral sensors Chapter 2 41 Nowadays, hyperspectral sensors are increasing their importance in remote sensing studies. ESA Chris-Proba 11 and Hyperion 12 were the first satellite missions dedicated to this sensor. However, their spatial resolution (30 meters for Hyperion, from 18 to 30 for Chris-Proba) cannot be considered as ―Very High Resolution‖, therefore no useful for the purposes of the study. For this reason, it has been decided to use airborne hyperspectral sensors, which combines very high spatial resolution with several contiguous bands. 2.2.1 AHS The Airborne Hyperspectral Scanner (AHS) is an imaging line-scanner radiometer, installed on a CASA-212 200 series aircraft owned by Spain's National Institute for Aerospace Technology (INTA) 13 . The AHS instrument (Fig. 2.5), with a whiskbroom linescanner, acquires images in 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid-infrared (MIR), and thermal infrared (TIR) spectral ranges (Table 1), having a digitisation accuracy of 12 bits. With its wide spectra, AHS has been used for several earth observation topics, as thermography [206] and agriculture [207]. 11 ESA Chris Proba home page: http://earth.esa.int/missions/thirdpartymission/proba.html 12 Nasa Hyperion Instrument: http://eo1.gsfc.nasa.gov/Technology/Hyperion.html 13 The INTA AHS system http://bo.eufar.net/document/publi/124901161346543eefc58cf:SPIE_RS05_INTA_5978-56.pdf
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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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