Hyperspectral imaging is an important technique in remote sensing which is characterized by high spectral resolutions. With the advent of new hyperspectral remote sensing missions and their increased temporal resolutions, the availability and dimensionality of hyperspectral data is continuously increasing. This demands fast processing solutions that can be used to compress and/or interpret hyperspectral data onboard spacecraft imaging platforms in order to reduce downlink connection requirements and perform a more efficient exploitation of hyperspectral data sets in various applications. Over the last few years, reconfigurable hardware solutions such as field-programmable gate arrays (FPGAs) have been consolidated as the standard choice for onboard remote sensing processing due to their smaller size, weight, and power consumption when compared with other high-performance computing systems, as well as to the availability of more FPGAs with increased tolerance to ionizing radiation in space. Although there have been many literature sources on the use of FPGAs in remote sensing in general and in hyperspectral remote sensing in particular, there is no specific reference discussing the state-of-the-art and future trends of applying this flexible and dynamic technology to such missions. In this work, a necessary first step in this direction is taken by providing an extensive review and discussion of the (current and future) capabilities of reconfigurable hardware and FPGAs in the context of hyperspectral remote sensing missions. The review covers both technological aspects of FPGA hardware and implementation issues, providing two specific case studies in which FPGAs are successfully used to improve the compression and interpretation (through spectral unmixing concepts) of remotely sensed hyperspectral data. Based on the two considered case studies, we also highlight the major challenges to be addressed in the near future in this emerging and fast growing research area.

The use of the Integer Karhunen-Loéve Transform (Integer KLT) for spectral decorrelation in lossless compression of hyperspectral satellite images results in improved performance. However, the Integer KLT algorithm consists of sequential processes, which are computationally intensive, such as the covariance and eigenvector evaluations, matrix factorisation and lifting. These processes slow down the overall computation of the Integer KLT transform significantly. The acceleration of these processes within the context of limited power and hardware budgets is the main objective of this paper. The computations of each of these processes are investigated thoroughly by breaking them down into primitive arithmetic operations. Subsequently, a comprehensive analysis of these computations is presented to inspect the possibility and feasibility of different hardware mappingoptions. The proposed designs are implemented on a System-on-a-Chip platform, which incorporates a 32-bit hardwired microcontroller and a co-processing unit built within a field programmable gate array fabric. A novel architecture is proposed offering accelerated processing and low power consumption. The proposed solution is not only suitable for space applications, but also for different mobile and medical applications.

Image compression is an important requirement of imaging payloads on board Earth Observation satellites. This paper presents a new on-board real-time compression system, capable of lossless and lossy image compression. A cost-effective lossless image compression scheme, based on the CCSDS recommendation, is proposed and tested with multi/hyperspectral images. An efficient hardware implementation is achieved using FPGA-based acceleration. The hardware accelerator design is optimized by employing novel techniques at algorithmic and logic levels.

Space and satellite systems are considered to be the most extreme environment to design for and are fraught with engineering difficulty. Performance metrics such as fault tolerance, reliability, pre-determinism and heritage are still high of the list of requirements for all missions. But with the advent of modern day electronics, greater computing capability and networking technologies have enabled research into distributed satellite systems, where multiple spacecraft work collaboratively to perform a mission. Leveraging from these technologies, a satellite can be considered one of many nodes in an autonomous system. This paper proposes the use of an Agent based computing platform to solve orbit dynamics problems leading to a highly mobile and decentralized system. The latest Agent platforms are compared and possible Agent applications are presented; specifically, distributed image compression and a novel topology reconfiguration scheme.

In the Consultative Committee for Space Data Systems - Lossless Data Compression (CCSDS-LDC) algorithm a small region of image data is compressed independently to achieve error-resilience functionality, which is one of the most distinct requirements for space applications. 2-D or 3-D predictions are believed to bring superior compression performance, however they interfere with the error-resilience property. This paper proposes a new scan scheme to address the problem. By combination of multidimensional predication and embedded radiometric calibration technique, the proposed system performs very effectively on remote sensing panchromatic and multispectral images. Finally a high-level reconfigurable model for the hardware implementation of the proposed system is developed targeted at FPGA devices.

A new dimension of wireless sensor network architecture design is emerging where hundreds to thousands of ultra-light low-cost sensor nodes are required to collectively perform a spectrum of distributed remote sensing missions in hostile conditions, predominantly those encountered in space. Research is underway to investigate the feasibility of fabricating survivable self-powered sensor nodes monolithically with commercially available SiGe BiCMOS technology. This paper presents simulation and test chip results of two novel and essential building blocks: a photovoltaic/solar cell power supply and an environmentally tolerant microprocessor, based on radiation hardening by design and asynchronous logic.

There is a continuing trend in the space community to reduce costs involved in space missions. Miniaturization has been the primary method that was deployed for reducing space mission costs. This is now changing, due to the recent advancements in technology that enable advanced mission architectures namely, distributed spacecraft missions. Several spacecraft flying in close formations would collaboratively achieve the mission aims at lower costs and with enhanced reliability compared to larger single platform missions. Distributed algorithms have been proposed for use on-board these missions for coordination and control purposes. No work has been done to implement a distributed system at the lower levels of abstraction. This paper proposes the deployment of distributed computing on-board close formation flying missions. Two distributed computing paradigms namely, client-server and mobile agent, are analytically compared in view of close formation flying missions. As a result the mobile agent paradigm is proposed for the application in hand.