Abstract
For many years, panchromatic aerial photographs have been the main source of remote sensing data for detailed inventories of urban areas. Traditionally, building extraction relies mainly on manual photo-interpretation which is an expensive process, especially when a large amount of data must be processed (Ameri, 2000). The characterization of a given object bases on its visible information, such as: shape (external form, outline, or configuration), size, patterns (spatial arrangement of an object into distinctive forms), shadow (indicates the outlines, length, and is useful to measure height, or slopes of the terrain), tone (color or brightness of an object, smoothness of the surface, etc.)(Ridd 1995). Automated assessment of urban surface characteristics has been investigated due to the high costs of visual interpretation. Most of those studies used multispectral satellite imagery of medium to low spatial resolution (Landsat-TM, SPOT-HRV, IRS-LISS, ALI and CHRIS-PROBA) and were based on common image-analysis techniques (eg maximum likelihood (ML) classification, principal components analysis (PCA) or spectral indices (Richards and Jia 1999)). The problems of limited spatial resolution over urban areas have been overcome with the wider availability of space-borne systems, which characterized by large swath and high spatial and temporal resolutions (eg Worl-View2). However, the limits on spectral information of nonvegetative material render their exact identification difficult. In this regard, the hyperspectral remote sensing (HRS) technology, using data from airborne sensors (eg AVIRIS, GER, DAIS, HyMap, AISA-Dual), has opened up a …
Original language | English |
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Title of host publication | Remote Sensing |
Subtitle of host publication | Advanced Techniques and Platforms |
Editors | Boris Escalante-Ramirez |
Publisher | IntechOpen |
Chapter | 2 |
Pages | 29-50 |
Number of pages | 21 |
ISBN (Electronic) | 9789535150039 |
ISBN (Print) | 9789535106524 |
DOIs | |
State | Published - 2012 |