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Image Segmentation: Errors, sensitivity, and uncertaintyProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991
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Quality Assessment of Despeckled SAR Images
No full text—In this paper, a novel method for the quality assessment of despeckled SAR images is proposed. This method is based
on the observation that the perceived quality of despeckled SAR
images is not always appropriately described by classical statistical and deterministic parameters that are proposed in the literature. Various evaluations are performed here. A preliminary visual
qualitative evaluation is taken as a reference for the subsequent
quantitative assessment. Then, a revised statistical analysis that
can solve some of the drawbacks of previous methods is proposed;
however, the statistical approach still has certain drawbacks. To
address this problem, a new frequency analysis approach is first
proposed, together with a definition of the appropriate indexes. In
this way, it is possible to select the best filter in terms of noise reduction, edge and texture preservation, while limiting the effect of
introduced distortions. While statistical analysis is widely used in
the literature, frequency analysis has never been presented for this
aim, especially for non-linear filters. We prove that frequency analysis can robustly identify the best filter, taking perceptual considerations into account, even when statistical analysis fails. Despeckling methods based on anisotropic diffusion algorithms are used for
a comparison, but the proposed analysis can be applied to any filtering method. Experiments are presented with SAR images from
the Italian Cosmo/Skymed constellation. Both Stripmap and Spotlight acquisitions have been evaluated, and to prove the validity of
the proposed method with respect to different spatial resolutions
and different classes of interest, various classes are considered
A New Method for Cross-Normalization and Multitemporal Visualization of SAR Images for the Detection of Flooded Areas
No full textWhenever multitemporal synthetic aperture radar (SAR) images are available, precise calibration and perfect spatial registration are required to obtain a useful image for displaying changes that have occurred. SAR calibration is a very complex and sensitive problem; some errors may persist after calibration that interfere with subsequent steps in the data fusion and visualization process. Because of the strong histogram asymmetry of SAR images, due to the well-known non-Gaussian model of radar backscattering, traditional image preprocessing procedures cannot be used here. A novel specific preprocessing phase, the so-called “cross-calibration/normalization,” is proposed to solve this problem. This, in turn, facilitates image enhancement and the numerical comparison of different image takes together with data fusion and visualization processes. The proposed processing chain includes filtering, histogram truncation, and equalization steps applied in an adaptive way to the images in question. The design of the method and the experimental procedure is based on images from the Italian Cosmo/Skymed mission. Both Stripmap and Spotlight images are taken into account to test the algorithms at different spatial resolutions. This paper also presents an example application: the generation of a single flood picture, the so-called “fast-ready flood map,” from multitemporal SAR images. The maps are very quickly and automatically generated without user interaction to support the authorities in providing first aid to a population. Toward this end, RGB composition is used: pre-flood and post-flood images are combined into a color image to better identify the flooded areas in comparison with permanent water and other classes
COSMO SKYMED IN SUPPORT OF FLOOD MONITORING
No full textIn this paper, the exploitation of Earth Observation (EO) data in the operational chains for flood monitoring and post-event damage assessment is addressed, focusing to the specific task of flood mapping.
The very-high-resolution (VHR) multitemporal observation capability offered by the current sensors, and in particular by Cosmo-SkyMed Synthetic Aperture Radar (SAR) constellation, is exploited.
Two image processing algorithms based on data fusion and segmentation, merged with a preliminary non-linear filtering, are presented for flood maps generation. Both approaches help to locate flooded areas from a pair of SAR images acquired before and after the event.
The work is framed in the context of a project funded by the Italian Space Agency. Experiments are performed on Cosmo-SkyMed images acquired in Stripmap configuration, related to different datasets acquired all over the world
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