Search

The Sentinel-1 mission has finally reached its maturity with the launch of the second Sentinel radar. Among the products delivered by the agency, the ground range detected class is raising more and more interest among users due to its reduced computational demand for information extraction and availability on cloud exploitation platforms, like the Google Earth Engine. In this paper, we present a novel multitemporal processing chain, suitable to be applied to Sentinel-1 ground range detected products to obtain RGB images, using a series of single polarization detected images. These products aim at being the equivalent for the recently introduced Level-1 $\alpha$ , exploiting a texture measure instead of the interferometric coherence, to properly render and enhance the presence of built-up areas. The discussion is supported by experiments showing the reliability of this newly introduced class of products in classic synthetic aperture radar applications like image photointerpretation, flood mapping, and long-term urban area monitoring. [ABSTRACT FROM AUTHOR]

This research adapts an effective change detection approach originally applied to mapping fire scar from a stationary synthetic aperture radar (SAR) scene to the problem of oil spills from SAR data. The method presented here combines several advanced image processing techniques to mitigate some of the common performance limitations of SAR-based oil spill detection. Principally among these limitations are the following. First, the radar cross section of the areas affected by an oil spill strongly depends on wind and wave effects, and is, therefore, highly variable. Second, the radar cross section of oil covered water is often indistinguishable from other dark ocean features such as low wind areas, which leads to errors and uncertainties in oil spill detection. In this paper, we introduce a multi-image analysis, the Lipschitz regularity (LR), and wavelet transforms as a combined approach to mitigate these performance limitations. We show that the LR parameter is much less sensitive to variations of wind and waves in an oil spill SAR imagery, lending itself well for normalizing and suppressing ocean background using image ratio processing. We combine LR processing with a multiscale technique based on the wavelet transform to additionally achieve high-quality noise suppression. To describe the performance of this approach under controlled conditions, we applied our method to simulated SAR data of wind-driven oceans with oil spills and low wind areas. We also applied our method to several real-world oil spill data acquired during the 2010 Deep Water Horizon spill in the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar (SAR) data. Its the purpose is to combine classic SAR processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of the Level-1 $\alpha$ and Level-1$\beta$ families, and employs self-organizing map clustering and object-based image analysis. In particular, the input products are clustered using color homogeneity and automatically enriched with a semantic attribute referring to clusters’ color, providing a preclassification mask. Then, in the frame of an application-oriented object-based image analysis, opportune layers measuring scattering and geometric properties of candidate objects are evaluated, and an appropriate rule-set is implemented in a fuzzy system to extract the feature of interest. The obtained results have been compared with those given by existing techniques and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semiarid environment. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a novel framework for small reservoirs extraction in semiarid environment. The task is accomplished through the introduction of a pseudoprobability index derived from multitemporal synthetic aperture radar RGB images. These products are characterized by the ease of interpretation for nonexpert users, and the possibility to be processed using simple algorithms, allowing, in this case, for the definition of an ad hoc band ratio for feature extraction. The reliability of the proposed approach is demonstrated through a case study in Burkina Faso in which 19 reservoirs up to about 6000 m$^2$ extent were tested. The obtained accuracy with respect to the available ground truth is higher than 88%. [ABSTRACT FROM PUBLISHER]

In this paper, we present a technique for improving the representation of built-up features in model-based multitemporal synthetic aperture radar (SAR) RGB composites. The proposed technique exploits the multitemporal adaptive processing (MAP3) framework to generate an a priori information which is used to implement an adaptive selection of the coherence window size. Image texture is used to support the coherence information in case of decorrelation. The coherence information, powered by texture analysis, and combined with backscattering amplitude, provides a unique representation of built-up features. This allows for an immediate detection of urban agglomerates by human operators, and is an advantaged starting point for urban area extraction algorithms. [ABSTRACT FROM PUBLISHER]

We propose a new approach for remote sensing data exploration, based on a tight human–machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All processing tools are in the framework of the tree-structured MRF model, which allows for a flexible and spatially adaptive description of the data. We test the proposed approach for the exploration of multitemporal COSMO-SkyMed data, that we appropriately registered, calibrated, and filtered, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods. [ABSTRACT FROM PUBLISHER]

With the launch of COSMO/SkyMed and TerraSAR-X missions, very high resolution (VHR) Synthetic Aperture Radar (SAR) images of urban areas have become routinely available. In particular, in the spotlight acquisition mode, COSMO/SkyMed SARs are able to obtain a resolution even better than one meter. Accordingly, in principle a huge amount of information on objects present in the urban scenario can be extracted from such images; however, due to the involved interaction between incident electromagnetic wave and imaged scene, direct interpretation of VHR SAR images is not straightforward, and different methods to analyze them are being developed by the scientific community. In this paper, we explore the potentiality of using the power spectral density (PSD) of VHR SAR amplitude images of urban areas for information extraction. First, we propose a theoretical model of the PSD of such images, and then we test the validity of the developed model on COSMO/SkyMed spotlight SAR images of urban areas. Finally, some suggestions are provided on how the parameters of the obtained PSD can be exploited to extract useful information from VHR SAR amplitude images of urban areas. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a methodology devoted to exploit the outstanding characteristics of COSMO-SkyMed for monitoring water bodies in semiarid countries at a scale never experienced before. The proposed approach, based on appropriate registration, calibration, and processing of synthetic aperture radar (SAR) data, allows outperforming the previously available methods for monitoring small reservoirs, mainly carried out with optical data, and severely limited by the presence of cloud coverage, which is a frequent condition in wet season. A tool has been developed for computing the water volumes retained in small reservoirs based on SAR-derived digital elevation model. These data have been used to derive a relationship between storage volumes and surface areas that can be used when bathymetric information is unavailable. Due to the lack of direct measures of river’s discharge, the time evolution of water volumes retained at reservoirs has been used to validate a simple rainfall–runoff hydrological model that can provide useful recommendation for the management of small reservoirs. Operational scenarios concerning the improvement in the efficiency of reservoirs management and the estimation of their impact on downstream area point out the applicative outcomes of the proposed method. [ABSTRACT FROM PUBLISHER]

The extremely remarkable properties of angle independence exhibited by an innovative SAR product, the fractal dimension map estimated from a single SAR image, are discussed. The theoretical analysis is supported by a noticeable data set of actual SAR images acquired, with look angles varying from 20^\circ to 45^\circ, in the stripmap operational mode by the COSMO-SkyMed constellation. The behavior of the fractal dimension maps at different look angles is discussed for both natural and urban scenarios and emphasis is also posed on areas within the same image that, according to the scene macroscopic topography, are characterized by different incidence angles. The whole analysis is aimed at highlighting, on the one hand, the specific independencies of natural surface fractal dimension maps from the look angle and from the local incidence angle, which can be very useful in information extraction and SAR post-processing techniques and, on the other hand, the different fractal dimension maps behavior whereas urban areas are analyzed. [ABSTRACT FROM PUBLISHER]