Constant Complexity Management of 2D HW Multitasking in Run-Time Reconfigurable FPGAs

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It amounts the identification of pure spectral signatures (endmembers) in the data, and the estimation of the abundance of each endmember in each (possibly mixed) pixel. A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular and widely used techniques for this purpose is the HySime algorithm but, due to the complexity and high dimensionality of hyperspectral scenes, this technique is computational expensive. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present the first FPGA design for the HySime algorithm. Our system includes a direct memory access (DMA) module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASAs airborne visible infrared imaging spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World trade center (WTC) in New York.Experimental results demonstrate that our hardware version of the HySime algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.Index Terms-Field-programmable gate arrays (FPGAs), hyperspectral imaging, HySime, number of endmembers estimation, reconfigurable hardware.

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“…For this last purpose, we need to perform rotations using steps matrix like the one show in (23), called Jacobi rotations…”

Section: B Implementation Of the Signal Subspace Estimationmentioning

confidence: 99%

“…The idea is that FPGAs can be reconfigured on the fly. This approach is called temporal partitioning [22], [23] or runtime reconfiguration [24]. Basically, the FPGA (or a region of the FPGA) executes a series of tasks one after another by reconfiguring itself between tasks [25].…”

Section: B Signal Subspace Estimationmentioning

confidence: 99%

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

González

López

Mozos

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The idea is that FPGAs can be reconfigured on the fly. This approach is called temporal partitioning [94], [95] or run-time reconfiguration [96]. Basically, the FPGA (or a region of the FPGA) executes a series of tasks one after another by reconfiguring itself between tasks [97].…”

Section: B Fpga-based Linear Unmixing Of Hyperspectral Imagesmentioning

confidence: 99%

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

et al. 2013

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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.

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Partition Based Dynamic 2D HW Multitasking Management

Roman

Mecha

Mozos

et al. 2006

9th EUROMICRO Conference on Digital System Design (DSD'06)

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Constant Complexity Management of 2D HW Multitasking in Run-Time Reconfigurable FPGAs

Cited by 4 publications

References 5 publications

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

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

Partition Based Dynamic 2D HW Multitasking Management

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