Remote sensing is developing rapidly due to progress in many interconnected fields. It encompasses the emergence of novel sensors, the evolution of sophisticated sensor platforms, and advances in signal and data processing. Progress in radar, optoelectronic, acoustic, and other sensor technologies is especially striking. Although these sensors now offer greater sensitivity, accuracy, resolution, data rates and dynamic ranges, they still exhibit inherent limitations. The integration of multi-sensor systems and the joint processing of their signals have long been recognized as an effective means of mitigating individual drawbacks while leveraging complementary strengths. The advent of new sensor types provides scientists and engineers with opportunities to create more capable, integrated multi-sensor architectures. At the same time, user expectations regarding coverage area or volume, resolution, accuracy, processing speed and overall system functionality continue to rise. Extended frequency bands, enhanced resolution and data rates, and the widespread deployment of distributed sensors have dramatically increased data volumes in modern multi-sensor networks. These developments pose fresh challenges for data-fusion algorithms, which must now incorporate the latest advances in big-data analytics, statistical estimation and artificial intelligence. This Special Issue presents a collection of papers offering fresh insights into these emerging trends in multi-sensor systems and data fusion, and will be of interest to the entire remote-sensing community.
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