Land Cover Classification Of
QuickBird Multispectral Data With
An Object-oriented Approach
Author(s)
E. Tarantino
Abstract
Recent satellite technologies have produced new data as an alternative to aerial
photographs. These Very High Resolution (VHR) data offer widely acceptable
metric characteristics for cartographic updating and increase our ability to map
land use in geometric detail and improve accuracy of local scale investigations.
Even if it is possible to distinguish spatial entities obscured in the past by low
resolution sensors, high spatial resolution involves a general increase of internal
variability within land covers. Moreover, accuracy of results may decrease on a
per-pixel basis. If the full potentiality of the new image data for urban land use
mapping is to be realised, new inferential remote-sensing analysis tools need to
be investigated.
This study analyses a procedure of classification which aims to reduce
negative impacts related to spectral ambiguity and spatial complexity of land
cover classes of VHR imagery. In order to avoid such disadvantages, an objectoriented
classification methodology on QuickBird multispectral data has been
implemented on a peripheral area of Bari, in the region of Apulia (Italy). The
analysis consists of two phases: multiresolution segmentation and nearest
neighbour classification of the resulting image-objects. With this procedure the
generated segments act as image objects whose physical and contextual
characteristics can be described by means of fuzzy logic. Validity and limitations
of such methodology for land cover classification is finally evaluated
1 Introduction
The applications of data obtained from satellite platforms at 300 to 600 miles
above the earth have remained limited for urban areas until now for a variety of
Keywords
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