Abstract
The objective of this research is to explore the capabilities of the hyperspectral imagery in mapping the urban impervious objects and identifying the surface materials using an object-oriented approach. The application is conducted to Toulouse city (France) within the HYEP research project in charge of using hyperspectral imagery for the environmental urban planning. The method uses the multi-resolution segmentation and classification algorithms. The first results highlight a high potential of the hyperspectral imagery in land cover mapping of the urban environment, especially the extraction of impervious surfaces. They, also, illustrate, that the object-oriented approach by means of the fuzzy logic classifier yields promising results in distinguishing the mean roofing materials based only on the spectral information. Conversely to the red clay tiles and metal roofs, which are easily identified, the concrete, gravel and asphalt roofs are still confused with roads.
Highlights
Remote sensing technology has a great potential to afford consistent and useful spatial information at different spatial and temporal scales
This study highlights the high potential of the simulated HYPXIM hyperspectral imagery for the urban land cover mapping, especially the extraction of impervious surfaces
Illustrates that the object-oriented classification approach by means of the fuzzy logic method yields promising results in identifying the mean construction roof’ materials based on the spectral information, only
Summary
Remote sensing technology has a great potential to afford consistent and useful spatial information at different spatial and temporal scales. It can help understanding the links between land use and infrastructure change and a variety of social, economic, demographic and environmental processes [1, 2]. This information derived from remotely sensed datasets leads to an improved observation and monitoring that can benefit applied urban planning and management [2]. In addition to the spatial accuracy of the sensor, the definition of the thematic mapping classes and the image classification accuracy require specific spectral characteristics ranging from the Visible Near-Infrared (VNIR) to Short Wave Infrared (SWIR) wavelength domains [7, 8]
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