Content area
Abstract
Satellite-borne synthetic aperture radar (SAR) technology has revolutionized remote sensing applications by providing high-resolution and all-weather imaging capabilities. With the increasing availability of SAR data, the need for efficient data processing has become crucial. Onboard processing has emerged as a promising solution to address the challenges associated with limited downlink capacity and final data products latency. Performing data processing and compression directly on the airborne platform reduces the raw data transmitted to ground stations, which offers several key benefits. First, it significantly reduces the data volume to be downlinked, optimizing the usage of limited bandwidth and minimizing transmission delays. Second, onboard processing enables faster access to processed data products, allowing for quicker decision-making and timely response to dynamic events, enhancing the real-time capabilities of the system that are particularly valuable in time-critical applications. This article discusses various onboard processing techniques employed in SAR systems and explores their challenges, like computational constraints, architectural impacts, power consumption, and algorithm optimization. Furthermore, it examines the potential future developments in onboard processing, such as the integration of artificial intelligence and machine learning techniques to enhance data analysis and decision-making capabilities. The advancements in onboard processing have the potential to revolutionize the way SAR missions are conducted. By leveraging these techniques, SAR systems can achieve improved operational efficiency, reduced data latency, and enhanced real-time capabilities. This article emphasizes the significance of onboard processing in meeting the growing demands of SAR applications and underscores its role in advancing remote sensing capabilities for various sectors, including environmental monitoring, disaster response, and surveillance.
Details
Artificial intelligence;
Data processing;
Image resolution;
Power consumption;
Radar;
Remote sensing;
Data analysis;
Disaster management;
Remote monitoring;
Machine learning;
Downlinking;
Decision making;
Data reduction;
Data compression;
Synthetic aperture radar;
Onboard;
Network latency;
Onboard data processing;
Onboard equipment;
Algorithms;
Compression;
Latency;
Real time;
SAR (radar);
Ground stations
; Furano, Gianluca 2
; Ghiglione, Max 2
; Zancan, Valentina 3
; Imbembo, Ernesto 2
; Ilioudis, Christos 1
; Clemente, Carmine 1
; Trucco, Paolo 3
1 University of Strathclyde, Glasgow, U.K.
2 European Space Agency, Paris, France
3 Politecnico di Milano, Milano, Italy