The retrieval of 3-D surface models of the Earth is a major issue of remote sensing. Some nice results have already been obtained at medium resolution with optical and radar imaging sensors. For instance, missions such as the Shuttle Radar Topography Mission (SRTM) or the SPOT HRS have provided accurate digital terrain models. The computation of a digital surface model (DSM) over urban areas is the new challenging issue. Since the recent improvements in radar image resolution, synthetic aperture radar (SAR) interferometry, which had already proved its efficiency at low resolution, has provided an accurate tool for urban 3-D monitoring. However, the complexity of urban areas and high-resolution SAR images prevents the straightforward computation of an accurate DSM. In this paper, an original high-level processing chain is proposed to solve this problem, and some results on real data are discussed. The processing chain includes three main steps, namely: (1) information extraction; (2) fusion; and (3) correction. Our main contribution addresses the merging step, where we aim at retrieving both a classification and a DSM while imposing minimal constraint on the building shapes. The joint derivation of height and class enables the introduction of more contextual information. As a consequence, more flexibility toward scene architecture is possible. First, the initial images (interferogram, amplitude, and coherence images) are converted into higher-level information mapping with different approaches (filtering, object recognition, or global classification). Second, these new images are merged into a Markovian framework to jointly retrieve an improved classification and a height map. Third, DSM and classification are improved by computing layover and shadow from the estimated DSM. Comparison between shadow/layover and classification allows some corrections. This paper mainly addresses the second step, while the two others are briefly explained and referred to already published papers. The results obtained on real images are compared to ground truth and indicate a very good accuracy in spite of limited image resolution. The major limit of DSM computation remains the initial spatial and altimetric resolutions that need to be made more precise
[1]
Donald Geman,et al.
Constrained Restoration and the Recovery of Discontinuities
,
1992,
IEEE Trans. Pattern Anal. Mach. Intell..
[2]
Henri Maître,et al.
Retrieval of building shapes from shadows in high resolution SAR interferometric images
,
2004,
IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.
[3]
Jun Zhang,et al.
A Markov random field model-based approach to image interpretation
,
1989,
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[4]
H. Maître,et al.
Extraction of urban elevation models from high resolution interferometric SAR images
,
2004
.
[5]
Paolo Gamba,et al.
Detection and extraction of buildings from interferometric SAR data
,
1999,
IEEE Trans. Geosci. Remote. Sens..
[6]
Paolo Gamba,et al.
Feature fusion to improve road network extraction in high-resolution SAR images
,
2006,
IEEE Geoscience and Remote Sensing Letters.
[7]
Fuk K. Li,et al.
Synthetic aperture radar interferometry
,
2000,
Proceedings of the IEEE.
[8]
Thierry Rabaute,et al.
Radar interferometry: limits and potential
,
1993,
IEEE Trans. Geosci. Remote. Sens..
[9]
Henri Maître,et al.
A new statistical model for Markovian classification of urban areas in high-resolution SAR images
,
2004,
IEEE Transactions on Geoscience and Remote Sensing.
[10]
R. Bolter.
Reconstruction of man-made objects from high resolution SAR images
,
2000,
2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).
[11]
J. Besag.
On the Statistical Analysis of Dirty Pictures
,
1986
.
[12]
Isabelle Bloch,et al.
A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors
,
1999,
IEEE Trans. Geosci. Remote. Sens..
[13]
M. Datcu,et al.
Information extraction from high resolution SAR data for urban scene understanding
,
2003,
2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas.