A novel paradigm for urban environment characterization using ascending and descending TerraSAR-X data

E. Angiuli; G. Trianni; P. Gamba

doi:10.1117/12.898481

26 October 2011 A novel paradigm for urban environment characterization using ascending and descending TerraSAR-X data

E. Angiuli, G. Trianni, P. Gamba

Proceedings Volume 8179, SAR Image Analysis, Modeling, and Techniques XI; 81790C (2011) https://doi.org/10.1117/12.898481
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

The new Very High Resolution radar satellites, with a spatial resolution up to 1 meter, give a unique opportunity in the context of urban applications. This paper presents an approach for automatic detection of built-up areas based on the analysis of single-polarized TerraSAR-X images. The proposed methodology includes a specific preprocessing of the SAR data and an automated image analysis procedure. The preprocessing aims at providing a multi-resolution texture layer based on the analysis of local speckle characteristics to automatically extract settlements. The technique is tested on 2 TerraSAR-X images acquired over the city of Pavia, northern Italy, in February3 2008. The overall accuracies between 78% and 85% for the derived city footprints demonstrate the high potential of the proposed analysis for built up areas detection. In addition, the joint use of both acquisitions allow to reach a total accuracy of 89%. Although the methodology needs to be further tested on different case studies, the investigation demonstrates the feasibility and the utility of the combined use of ascending and descending SAR intensities data for complete urban footprint extraction.

Citation Download Citation

E. Angiuli, G. Trianni, and P. Gamba "A novel paradigm for urban environment characterization using ascending and descending TerraSAR-X data", Proc. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, 81790C (26 October 2011); https://doi.org/10.1117/12.898481

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KEYWORDS

Synthetic aperture radar

Image classification

Speckle

Backscatter

Buildings

Neural networks

Spatial resolution

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