Abstract

Advances in sensor technology are revolutionizing the way remotely sensed images are collected, managed and processed. The incorporation of latest generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional image data [6], and this explosion in the amount of collected information has rapidly created new processing challenges [4]. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in planetary exploration science, require real or near realtime processing capabilities. Relevant examples include monitoring of natural disasters, such as earthquakes and floods, military applications, tracking of man-induced hazards, such as wild land and forest fires, oil spills and other types of biological agents. Most of these applications require timely responses for swift decisions, which depend on realtime performance of algorithm analysis. Owing to the diversity and large dimensionality of the image data sets provided by observation instruments in Earth and planetary applications, there are no commonly accepted real-time image processing architectures and techniques in the context of remote sensing missions. In future years, remote sensing imaging spectrometers [2] such as hyperspectraland ultraspectral imagers [1] will continue increasing their spatial and spectral resolutions (imagers with thousands of narrow spectral bands are currently under development), thus producing a nearly continual stream of high-dimensional image data [5] and increasing the possibility of exploitation of the data with regards to multi-spectral imagers [3]. Such technological advances are not only expected in optical remote sensing instruments, but also in radar and other types of remote sensing systems. Such explosion in the amount, size and dimensionality of the information collected on a daily basis presents new challenges for real-time processing in certain remote sensing applications, which demand for quick response in practical exploitation. This special issue on ‘‘Architectures and techniques for real-time processing of remotely sensed images’’ is intended to present the state of the art and the most recent developments in the incorporation of (near) real-time image processing techniques and specialized architectures in the context of remote sensing applications. This special issue covers different techniques and architectures for (near) real-time image processing in remote sensing. The techniques addressed in the special issue deal with relevant problems in Earth observation, such as damage mapping in case of earthquakes, flood monitoring, target and anomaly detection for military applications, cloud cover assessment or precision agriculture. The set of implementation strategies discussed in the special issue is also extensive, including parallel and distributed platforms, grid computing and multi-core environments, and specialized architectures for real-time on-board processing such as field programmable gate arrays (FPGAs) or commodity graphics processing units (GPUs). Combined these topics are expected to provide an excellent snapshot of the state of the art in those areas, and to offer a thoughtful perspective of the potential and emerging challenges of applying real-time image processing architectures and algorithms to realistic remote sensing problems. The special issue comprises eight articles contributed by authors with extensive experience in the field of efficient A. J. Plaza (&) Department Technology of Computers and Communications, Escuela Politecnica, University of Extremadura, Avenida de la Universidad s/n, 10071 Caceres, Spain e-mail: aplaza@unex.es URL: http://www.umbc.edu/rssipl/people/aplaza

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