Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Wider coverage of observation missions will increase onboard power restrictions while, at the same time, pose higher demands from the perspective of processing time, thus asking for the exploration of novel high-performance and low-power processing architectures. In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures. To meet onboard processing restrictions, we employ a low-power Digital Signal Processor (DSP), comparing processing time and energy consumption with those of a representative set of commodity architectures. We demonstrate that DSPs offer a fair balance between ease of programming, performance, and energy consumption, resulting in a highly appealing platform to meet the restrictions of current missions if onboard processing is required.

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

[2]  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.

[3]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[4]  Antonio J. Plaza,et al.  A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Paul E. Johnson,et al.  A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures , 1983 .

[6]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[7]  Chein-I Chang,et al.  Field Programmable Gate Arrays (FPGA) for Pixel Purity Index Using Blocks of Skewers for Endmember Extraction in Hyperspectral Imagery , 2008, Int. J. High Perform. Comput. Appl..

[8]  M. E. Daube-Witherspoon,et al.  An iterative image space reconstruction algorithm suitable for volume ECT.IEEE Trans. , 1986 .

[9]  Robert A. van de Geijn,et al.  Level-3 BLAS on the TI C6678 Multi-core DSP , 2012, 2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing.

[10]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[11]  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.

[12]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[13]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[14]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[15]  Antonio J. Plaza,et al.  Commodity cluster-based parallel processing of hyperspectral imagery , 2006, J. Parallel Distributed Comput..

[16]  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..

[17]  Qian Du,et al.  Foreword to the Special Issue on High Performance Computing in Earth Observation and Remote Sensing , 2011, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

[18]  Chein-I Chang,et al.  Constrained subpixel target detection for remotely sensed imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[19]  Francisco Tirado,et al.  GPU for Parallel On-Board Hyperspectral Image Processing , 2008, Int. J. High Perform. Comput. Appl..

[20]  Robert A. van de Geijn,et al.  Unleashing the high-performance and low-power of multi-core DSPs for general-purpose HPC , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[21]  Antonio J. Plaza,et al.  Clusters Versus FPGA for Parallel Processing of Hyperspectral Imagery , 2008, Int. J. High Perform. Comput. Appl..

[22]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..