Roberto Del Prete

University of Naples Federico II | UNINA · Department of Industrial Engineering

Satellite-based onboard data processing is crucial for time-sensitive applications requiring timely and efficient rapid response. Advances in edge artificial intelligence are shifting computational power from ground-based centers to on-orbit platforms, transforming the "sensing-communication-decision-feedback" cycle and reducing latency from acquis...

Nowadays, there is growing interest in applying Artificial Intelligence (AI) on board Earth Observation (EO) satellites for time-critical applications, such as natural disaster response. However, the unavailability of raw satellite data currently hinders research of lightweight pre-processing techniques and limits the exploration of end-to-end pipe...

Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands. However, the computational complexity of traditional methods makes the algorithms difficult to be deployed on board satellites. This is esp...

Over the past decade, there has been a rapid acceleration in the development of Artificial Intelligence (AI) for Earth Observation (EO), driven by the exponential growth in collected data and advances in algorithms and computing. This revolution extends to onboard EO spacecraft, enabling the development of smart satellites and software-defined miss...

Automated Band-to-Band alignment is a crucial prerequisite for satellite mission involving image operations. Many techniques are now developed for image registration: correlation-based methods, feature-based methods, hybrid methods and Deep Learning-based methods. Due to perturbations in the spacecraft orientation and dynamic disturbances, the a pr...

Nowadays, most of the datasets leveraging space-borne Earth Observation (EO) data are based on high-end levels products, which are ortho-rectified, coregistered, calibrated, and further processed to mitigate the impact of noise and distortions. Nevertheless, given the growing interest to apply Artificial Intelligence (AI) onboard satellites for tim...

In the framework of maritime surveillance, vessel detection techniques based on spaceborne synthetic aperture radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. This paper deals with this topic, presenting a novel approach that exploits a cascade application of a pre-screening algo...

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discr...

The wakes generated by moving vessels represent relevant patterns in remotely sensed images. They are a marker of ship presence and can be processed to infer route, speed, size, and type of ships. Automatic wake detection can be exploited by law enforcement agencies and local authorities for ensuring a wide range of applications, including maritime...

In the framework of autonomous spacecraft navigation, this manuscript proposes a novel vision-based terrain relative navigation (TRN) system called FederNet. The developed system exploits a pattern of observed craters to perform an absolute position measurement. The obtained measurements are thus integrated into a navigation filter to estimate the...

Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. Even if deep learning (DL) has led to an impressive pe...