In this paper, a geometric and electromagnetic model of a typical element of urban structure is presented, in order to analytically evaluate in closed form its electromagnetic return to an active microwave sensor. This model can be used to understand what information on geometric and dielectric properties of a building can be extracted from microwave remote sensing data. The geometrical model consists of a rectangular parallelepiped whose vertical walls form a generic angle with respect to the sensor line of flight. The parallelepiped is placed on a rough surface. The radar return from such a structure can be decomposed into single-scattering contributions from the (rough) ground, the building roof (a plane surface in our model), and vertical walls and multiple scattering contributions from dihedral structures formed by vertical walls and ground. In our model, single-scattering contributions are evaluated by using either physical optics (PO) or geometrical optics (GO), depending on surface roughness. In order to account for multiple scattering between buildings and terrain, we use GO to evaluate the field reflected by the smooth wall toward the ground (first bounce) or the sensor (second or third bounce) and GO or PO (according to ground surface roughness) to evaluate the field scattered by the ground toward the wall (first or second bounce) or the sensor (second bounce). Finally, the above model is used to analyze the field backscattered from a building as a function of the main scene parameters; in particular, the angle between vertical walls and sensor line of night and the dependence on the look angle are analyzed.

We present a new methodology for rapid flood mapping exploiting Sentinel-1 synthetic aperture radar data. In particular, we propose the usage of ground range detected (GRD) images, i.e., preprocessed products made available by the European Space Agency, which can be quickly treated for information extraction through simple and poorly demanding algorithms. The proposed framework is based on two processing levels providing event maps with increasing resolution. The first level exploits classic co-occurrence texture measures combined with amplitude information in a fuzzy classification system avoiding the critical step of thresholding. The second level consists of a change-detection approach applied to the full resolution GRD product. The discussion is supported by several experiments demonstrating the potentiality of the proposed methodology, which is particularly oriented toward the end-user community.

A model for electromagnetic scattering from natural rough surfaces described by means of fractional Brownian motion model is developed. The fractal surface model is employed to obtain the Kirchhoff solution of the Stratton-Chu scattering integral. An analytical viable formulation is achieved and compared to available classical solutions. Comparison with experimental data is also provided. Results show advantages of proposed solution from both theoretical and experimental viewpoin

Synthetic aperture radar (SAR) raw signal simulation is a powerful tool for design of oil slick detection and interpretation systems. In this paper, the ocean simulation issues are presented, and the main problems relating to the oil presence on the sea surface are treated. Attention is focused on the electromagnetic side of the problem, taking account of the sensor signatures, the dielectric, physical-chemical, and geometric nature of the oil slick, and the environmental conditions. The presented SAR simulator is based on an ocean model and an oil slick model. The former makes use of multiscale description of the ocean surface: the distributed surface model for the SAR-ocean interaction is considered by taking into account the nonlinear hydrodynamic effect for the water particle movement. The latter model implements a modification of the ocean spectrum, based on the Marangoni theory and accounting for the nonlinear wave interaction mechanism. However, the proposed SAR raw signal simulator is modular and flexible, thus allowing other possible physical models for modeling the oil slick effect over the ocean spectrum. Meaningful SAR simulation experiments are presented and discussed, elucidating the role of difference on pollutants, oil thickness, wind speed and direction, incident wavelength and angle and other radar parameters. Validation of the simulator is also presented by comparison with experimental data. A striking conclusion of the paper is that higher order moments (from the second on) of oil slick SAR image statistics are quite different compared to those pertinent to an equivalent wind speed decrease on the imaged area. This suggests a convenient way to define new appropriate oil slick signatures.

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing, and data fusion techniques, is provided in a companion paper, titled: "Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques".