The stable point network (SPN) is a persistent scatterer interferometric technique developed by ALTAMIRA INFORMATION in 2001. The technique makes use of both ERS SAR and/or ASAR differential phase measurements to generate long term terrain movement and precise height maps with the same resolution as the original SAR images. The algorithm is capable to use all the available phase information even in conditions of large baselines or platform instabilities giving place to large Doppler centroid variations. Such behaviour is handled by precise location estimate of the scatterer within the pixel and accurate elevation extraction which permits the exact location of the radar measurement in ground geometry. However not all the SPN interferometric measurements within a pixel have a direct correspondence in the real scene. Some points presented as measurement points may not be related to existing structures on ground but directly generated by the processing itself. For instance, several artifacts can be created in the Synthetic Aperture Radar images due to the signal acquisition system. Azimuth ambiguities are one of them. They may appears as strong targets in low backscattering areas like forest or water. Secondary lobes of strong targets are also an issue. Since low level signals can be masked by the side lobes of higher level signals. They cannot be handled like standard scatterers and present completely different geometric behaviour not related to their position in the radar image. This paper discusses the way to identify those artifacts and analyses their impact on SPN measurements compared to their reference points (centre of the main lobe).
[1]
Fabio Rocca,et al.
Analysis of Permanent Scatterers in SAR interferometry
,
2000,
IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).
[2]
Jordi Inglada,et al.
High Resolution Differential Interferometry using Time Series of ERS and Envisat SAR Data
,
2004
.
[3]
Andrea Monti Guarnieri,et al.
Adaptive removal of azimuth ambiguities in SAR images
,
2005,
IEEE Transactions on Geoscience and Remote Sensing.
[4]
R. Scheiber,et al.
Sidelobe Suppression Using the SVA Method for SAR Images and Sounding Radars
,
2006
.
[5]
Alberto Moreira,et al.
Suppressing the azimuth ambiguities in synthetic aperture radar images
,
1993,
IEEE Trans. Geosci. Remote. Sens..