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

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 infra-red 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.

[1]  Antonio J. Plaza,et al.  Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing , 2013, Integr..

[2]  Masih Rahmaty,et al.  FPGA based singular value decomposition for image processing applications , 2008, 2008 International Conference on Application-Specific Systems, Architectures and Processors.

[3]  Antonio J. Plaza,et al.  Parallel Hyperspectral Image and Signal Processing [Applications Corner] , 2011, IEEE Signal Processing Magazine.

[4]  Pawel Dabal,et al.  A chaos-based pseudo-random bit generator implemented in FPGA device , 2011, 14th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems.

[5]  Chein-I Chang,et al.  High Performance Computing in Remote Sensing , 2007, HiPC 2007.

[6]  Francky Catthoor,et al.  Efficiently scheduling runtime reconfigurations , 2008, TODE.

[7]  Antonio J. Plaza,et al.  High-performance computing in remotely sensed hyperspectral imaging: the Pixel Purity Index algorithm as a case study , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[8]  Luca Sterpone,et al.  A Novel Fault Tolerant and Runtime Reconfigurable Platform for Satellite Payload Processing , 2013, IEEE Transactions on Computers.

[9]  Qian Du,et al.  High Performance Computing for Hyperspectral Remote Sensing , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Carlos González,et al.  A task graph execution manager for reconfigurable multi-tasking systems , 2010, Microprocess. Microsystems.

[11]  Hortensia Mecha,et al.  A vertex-list approach to 2D HW multitasking management in RTR FPGAs , 2003 .

[12]  Antonio J. Plaza,et al.  FPGA Design of an Automatic Target Generation Process for Hyperspectral Image Analysis , 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.

[13]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Sebastián López,et al.  A Novel Architecture for Hyperspectral Endmember Extraction by Means of the Modified Vertex Component Analysis (MVCA) Algorithm , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Antonio J. Plaza,et al.  The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends , 2013, Proceedings of the IEEE.

[16]  Carlos González,et al.  A Hardware Implementation of a Run-Time Scheduler for Reconfigurable Systems , 2011, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[17]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[19]  Antonio J. Plaza,et al.  Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs , 2012, Journal of Real-Time Image Processing.

[20]  Antonio J. Plaza,et al.  FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis , 2010, EURASIP J. Adv. Signal Process..

[21]  G. Iles,et al.  Experience powering Xilinx Virtex-7 FPGAs , 2013 .

[22]  Jürgen Becker,et al.  Impact of Task Distribution, Processor Configurations and Dynamic Clock Frequency Scaling on the Power Consumption of FPGA-based Multiprocessors , 2010, ReCoSoC.

[23]  Antonio J. Plaza,et al.  FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Hortensia Mecha,et al.  Constant Complexity Management of 2D HW Multitasking in Run-Time Reconfigurable FPGAs , 2006, ARC.

[25]  John F. Arnold,et al.  Reliably estimating the noise in AVIRIS hyperspectral images , 1996 .

[26]  Antonio J. Plaza,et al.  Recent Developments in High Performance Computing for Remote Sensing: A Review , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Rob Heylen,et al.  Hyperspectral Intrinsic Dimensionality Estimation With Nearest-Neighbor Distance Ratios , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Antonio J. Plaza,et al.  FPGA Implementation of Abundance Estimation for Spectral Unmixing of Hyperspectral Data Using the Image Space Reconstruction Algorithm , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Chein-I Chang.  Exploration of virtual dimensionality in hyperspectral image analysis , 2006, SPIE Defense + Commercial Sensing.

[30]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.