Whenever 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
A multichannel image segmentation method is proposed that utilizes Markov random fields (MRF's) with adaptive neighborhood (AN) systems. Bayesian inference is applied to realize the combination of evidence from different knowledge sources. In such a way, optimization of the shape of a neighborhood system is achieved by following a criterion that makes use of the Markovian property exploiting the local image content. The MRF segmentation approach with AN systems (MRF-AN) makes it possible to better preserve small features and border areas. The purpose of the paper is to show the usefulness of the concept of MRF-AN for SAR image segmentation.
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