Francesca Cecinati

Urban Areas Enhancement in Multitemporal SAR RGB Images Using Adaptive Coherence Window and Texture Information

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.

Multitemporal Level-

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1β products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonens self-organizing maps dealing with classification problems.

1\beta

Earth observation technologies can provide a significant contribution to the monitoring urban areas and critical infrastructures. In this paper, we show how to exploit the recently introduced multitemporal SAR RGB images of the Level-1α and Level-1β family in these applications. Simple, ad hoc algorithms are discussed to adapt these generalist products to the specific case study. In particular, self-organizing map clustering and object-based image analysis are used for urban area mapping. As for infrastructure monitoring, an application concerning railway monitoring is discussed.

Products: Definitions, Interpretation, and Applications

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ=0.1 and the singularity analysis are not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ=0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific dataset are used.

Multitemporal synthetic aperture radar for urban planning and critical infrastructure monitoring

In this paper, we present a new framework for high-level processing of time series images, with particular reference to Sentinel-1 data. The proposed methodology has the goal of enhancing the interpretation of SAR imagery through the production of physical-based RGB composites, which are particularly suited for being easily interpreted by the human photo-interpreter, lowering the expertise level required for managing SAR data.

Comparing Approaches to Deal with Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging

In this paper, we explore the possibility to exploit GEOBIA concepts for extracting features from multitemporal SAR images. The proposed processing chain is feed by the recently introduced products of the Level-1a and Level-1β families and aims at providing an unsupervised tool for information extraction particularly oriented toward the end-user community. The principal characteristics and the effectiveness of the framework are illustrated through two examples concerning urban area mapping and small reservoir extraction in semiarid environment.

Sentinel-1 multitemporal SAR products

In this paper, we present a new framework for the generation of two new classes of RGB products derived from multitemporal SAR data. The aim of our processing chain is to provide products characterized by a high degree of interpretability (thanks to a consistent rendering of the underlying electromagnetic scattering mechanisms) and by the possibility to be exploited in combination with simple algorithms for information extraction. The physical rationale of the proposed RGB products is presented through examples highlighting their principal properties. Finally, the suitability of these products with applications is demonstrated through two examples dealing with feature extraction and classification activities.