TOR VERGATA UNIVERSITY UNSUPERVISED CLASSIFICATION ...

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8 The retrieval of information in urban areas: a challenging issue Ever since the first multispectral imagery became available from civilian remote sensing satellites in the early 1970s, considerable effort has been devoted to supervised classification of image data with the aim of producing high-quality thematic maps and establishing accurate inventories of spatial classes [21]. There have been advances under several aspects of supervised satellite image classification, developing components of the classification algorithm including the training or learning strategy and approaches to class separation based on statistical or other estimators and class separability indexes. But today, the continuous increase in archives' size and EO sensors' variety, requires new methodologies and tools for information mining and management. The manual process performed by experts to mine information from images is currently too complex and expensive to be applied systematically on even a small subset of the acquired scenes. The issue might become even more challenging in future since more missions - including constellations - are being planned, with broader sensor variety, higher data rates and increasing complexity. With this growth, new approaches for image information mining, multi-domain information management, and knowledge management and sharing (in support of information mining and training) are necessary, allowing the user demand for simultaneous access to multi-domain data. An automatic chain reduces the human assistance in the data analysis, allows to gain time on the entire image processing and can be designed to retrieve information in Near Real Time. Since from the late 60‘s, unsupervised methods have been developed [22]: they have assumed more importance with the above mentioned reasons and now they represents one of the focal point in feature identification in urban areas [14].
Introduction 9 The obtained classified maps of urban area can be used for several purposes, as urban planning [23], crisis management or in support to civil protection activities [24], human settlement damage [25] and urban thermography [26]. To summarize, the idea of this study is to show how to retrieve information on the land cover from VHR images (multispectral and hyperspectral), which is possible exploiting the spectral, textural and shape information from the starting images. After a preprocessing stage, these data constitute the input for an original automatic classification tool: the proposed processing chain is able to return as output a classified image, that is a valuable source of information for decision-making processes, urban planning and NRT monitoring.
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92 5.1 Vegetation Spectral, textura
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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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168 Conclusions Conclusions In this
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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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