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• Cavalli, RMaria
• Fusilli, L
• Pascucci, S
• Pignatti, S
• Santini, F
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• Cavalli, RMaria
• Fusilli, L
• Pascucci, S
• Pignatti, S
• Santini, F
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• Cavalli, RMaria
• Fusilli, L
• Pascucci, S
• Pignatti, S
• Santini, F

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Sensors 2008, 8(5), 3299-3320; doi:10.3390/s8053299

Article

Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

Rosa Maria Cavalli ¹ , Lorenzo Fusilli ¹ , Simone Pascucci ^2,* , Stefano Pignatti ²  and Federico Santini ¹

¹ National Research Council, Institute of Atmospheric Pollution, Via Fosso del Cavaliere, 100, Roma, 00133, Italy ² National Research Council, Institute of Methodologies for Environmental Analysis, C.da S. Loja - Zona Industriale, Tito Scalo (PZ), 85050, Italy

* Author to whom correspondence should be addressed.

Received: 17 April 2008 / Accepted: 15 May 2008 / Published: 20 May 2008

(This article belongs to the Special Issue Sensors for Urban Environmental Monitoring)

Download PDF Full-Text [1209 KB, uploaded 16 September 2008 11:02 CEST]

Abstract: This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.

Keywords: urban environmental monitoring; satellite hyperspectral remote sensing; object-oriented classification; band-depth analysis; linear spectral unmixing.

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