Silvana G. Dellepiane

Coastline extraction from SAR images and a method for the evaluation of the coastline precision

The coast area is a vital and highly dynamic environment whose multiple geophysical parameters are worth monitoring. At present the current coastline extraction operations made through high-resolution aerial images consist of the visual photo-interpretation. This performance, which mainly finds cartographic applications, is rather slow in comparison to the possibilities of remote sensing and image processing techniques. The aim of this paper is to describe the development and testing of an innovative algorithm able to extract semiautomatically the coastline by means of remote sensed images. The approach proposed is based on fuzzy connectivity concepts and takes into account the coherence measure extracted from an InSAR (Interferometric Synthetic Aperture Radar) couple. The method combines uniformity features and the averaged image that represents a simple way of facing textural characteristics. The results are then quantitatively evaluated through the comparison with optical aerial images. An automatic procedure is proposed for the evaluation of results, which makes use of distance measurements between the satellite and the aerial result, even though there is a considerable difference in space resolution.

Synthetic aperture radar image segmentation by a detail preserving Markov random field approach

A multichannel image segmentation method is imposed that utilizes Markov random fields (MRFs) 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.

Extraction of intensity connectedness for image processing

Abstract A modification to the traditional concept of fuzzy connectedness is described, which extends the basic ideas to grey-level objects. In addition, a non-iterative method for detection of suboptimal paths is proposed, which simplifies the computation of such a measure.

Nonlinear image labeling for multivalued segmentation

We describe a framework for multivalued segmentation and demonstrate that some of the problems affecting common region-based algorithms can be overcome by integrating statistical and topological methods in a nonlinear fashion. We address the sensitivity to parameter setting, the difficulty with handling global contextual information, and the dependence of results on analysis order and on initial conditions. We develop our method within a theoretical framework and resort to the definition of image segmentation as an estimation problem. We show that, thanks to an adaptive image scanning mechanism, there is no need of iterations to propagate a global context efficiently. The keyword multivalued refers to a result property, which spans over a set of solutions. The advantage is twofold: first, there is no necessity for setting a priori input thresholds; secondly, we are able to cope successfully with the problem of uncertainties in the signal model. To this end, we adopt a modified version of fuzzy connectedness, which proves particularly useful to account for densitometric and topological information simultaneously. The algorithm was tested on several synthetic and real images. The peculiarities of the method are assessed both qualitatively and quantitatively.

A New Method for Cross-Normalization and Multitemporal Visualization of SAR Images for the Detection of Flooded Areas

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.

Model generation and model matching of real images by a fuzzy approach

Abstract A method based on fuzzy sets for model representation and matching of real images in a knowledge-based system is presented. The detailed descriptions of the systems models, data structures, and matching mechanism, as well as the introduction to a method for the generation of symbolic models, are the main topics of the present paper. Models and data structures are based on the use of fuzzy restrictions. The process of model generation starts from a set of training images whose features are analysed to find discriminant descriptions of single objects and their mutual relationships. As an example of application, the efficiency of this approach has been tested using medical tomographic images acquired by the magnetic resonance technique. Results demonstrate the applicability of one ideal model to various real scenes of the same type. The systems performance and the errors incurred are evaluated. The robustness of the model and of the method has been proved both by processing images affected by noise and by changing segmentation threshold values in the preprocessing step.

Information Extraction From Remote Sensing Images for Flood Monitoring and Damage Evaluation

Satellite remote sensing missions devoted to Earth observation (EO) currently offer a unique capability to monitor the evolution of the Earths surface by providing temporally repetitive views at the desired (global, regional, or local) spatial scale. This wealth of remote sensing data conveys a huge potential for preventing, monitoring, and managing natural or man-made disasters. Specifically focusing on flood risk, a successful exploitation of this potential requires not only accurate and reliable image-analysis methods to extract the desired thematic information, but also the ability to combine this information with physically based models of the observed processes. Therefore, a multidisciplinary approach combining remote sensing with geophysical sciences, such as, in this case, hydrometeorology, is fundamental. This combination of expertise allows, in particular, satellite data to be exploited within the different phases of flood risk reduction: risk assessment, prevention, mitigation, monitoring, and management. In this paper, we investigate the key issues involved in the exploitation of satellite data with special focus on the phases of the emergency and post-disaster damage assessment. To this end, the challenges and the methodological approaches involved in the multidisciplinary combination of image analysis and hydrometeorology are discussed with the purpose of guiding and optimizing the process of information extraction from satellite data according to the requirements of civil protection from floods. Experimental examples of a few relevant case studies are also presented.

SAR images and interferometric coherence for flood monitoring

SAR backscatter intensity has already been successfully exploited for the detection of different types of changes in a scene, including seasonal changes, ice floes, landslides, earthquake damage and flooding. A drawback is that the backscatter intensity is affected by the presence of wind fields, water. Flood detection performed by only involving SAR intensity values could be not reliable, unless the weather information is integrated. On the contrary, interferometric coherence, usually low in presence of water, is not sensitive to weather conditions. The authors demonstrate that water can be easily identified by a data fusion which involves multitemporal backscatter intensities and multitemporal interferometric coherence: the additional information provided by the absence of coherence over water allows for a more accurate identification of the flooded areas with respect to simply exploiting the backscatter intensity.

Quality Assessment of Despeckled SAR Images

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.

Design and Implementation of Web-Based Systems for Image Segmentation and CBIR

This paper is focused on the design and development of an architecture that is able to provide remote segmentation service to various kinds of images or applications. The system exploits the functionalities offered by the already existing desktop application Isocontour by extending its capabilities toward a client-server environment. In order to achieve this goal, the original Isocontour code has been decomposed to independent modules for processing, storing, and representing purposes. By using the created server components, a web application has been developed to demonstrate how to make the fuzzy image-segmentation service highly available through the Internet. Furthermore, by exploiting the same architecture seen for the Isocontour algorithm, a remote content-based retrieval service for image databases was implemented in order to show the adaptability of this web-based system. Performance evaluation in terms of processing times with different image sizes has been performed in order to compare the web-based solution with the stand-alone one and to prove the reliability of the proposed system