The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends

Abstract

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 on board 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 on-board 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.

Hyperspectral Remote Sensing Data Analysis and Future Challenges

“…All these complex interactions can be studied through the definition of physical parameters representing different properties for land (e.g., surface temperature, biomass, leaf area coverage), water (e.g., yellow substance, ocean color, suspended matter) or the atmosphere (e.g., temperature and moisture profiles at different altitudes). The field of physical parameter estimation is an intermediate modeling step necessary to transform the measurements 13 into useful estimates [157].…”

“…Solving the inversion problem implies the design of algorithms that, starting from the radiation acquired by the sensor, can give accurate estimates of the variables of interest, thus "inverting" the RTM. In the inversion process, a priori information of the variables of interest can also be Observation Configuration 13 The acquired data may consist of multispectral or hyperspectral images provided by satellite or airborne sensors, but can also integrate spectra acquired by in situ (field) radiometers, GIS data that help to integrate geographical information, or radiosonde measures for weather monitoring. included to improve the performance, such as the type of surface, geolocation, or acquisition time.…”

“…Similar data volume ratios are expected for EnMAP and PRISMA. Unfortunately, this extraordinary amount of information jeopardizes the use of latest-generation hyperspectral instruments in real-time or near real-time applications, due to the prohibitive delays in the delivery of Earth Observation payload data to ground processing facilities [13]. In this respect, the European Space Agency (ESA) already flagged up in 2011 that "data rates and data volumes produced by payloads continue to increase, while the available downlink bandwidth to ground stations is comparatively stable" [14].…”

Error Resilient GPU Accelerated Image Processing for Space Applications

“…Currently, in the space sector the field programmable gate array (FPGA) is the established standard choice hardware for onboard data processing [5]. In addition to providing flexible computing with often a low power, volume and mass package, FPGAs can be manufactured to provide SEE resilience at a hardware level.…”

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

“…Hardware solutions have to conform to a number of satellite environment-induced constraints such as minimised size, mass, power consumption and tolerance to radiation effects. In recent years, the field programmable gate array (FPGA) has emerged as the standard choice for onboard image processing [3]. FPGA's have gained wide spread adoption due to their relatively small size, mass, power consumption, radiation hardness and re-configurability.…”