Comparative study on the performance of multiparameter SAR Data for operational urban areas extraction using textural features

Abstract : The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF)model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m.
Document type :
Journal articles
Liste complète des métadonnées

Cited literature [18 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00456174
Contributor : Import Ws Irstea <>
Submitted on : Friday, February 12, 2010 - 3:56:18 PM
Last modification on : Thursday, April 11, 2019 - 12:34:34 PM
Document(s) archivé(s) le : Friday, June 18, 2010 - 8:24:07 PM

File

MT2009-PUB00027136.pdf
Files produced by the author(s)

Identifiers

Citation

C. Corbane, N. Baghdadi, Xavier Descombes, G.J. Wilson, N. Villeneuve, et al.. Comparative study on the performance of multiparameter SAR Data for operational urban areas extraction using textural features. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2009, 6 (4), p. 728 - p. 732. ⟨10.1109/LGRS.2009.2024225⟩. ⟨hal-00456174⟩

Share

Metrics

Record views

710

Files downloads

382