Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing (original) (raw)

References

  1. Chang, C. I. (2003). Hyperspectral imaging: Techniques for spectral detection and classification (Vol. 1). Baltimore: Springer Science & Business Media, University of Maryland.
    Book Google Scholar
  2. Gomez, C., Rossel, R. A. V., & McBratney, A. B. (2008). Soil organic carbon prediction by hyperspectral remote sensing and field Vis-NIR spectroscopy: An Australian case study. Geoderme, 146(3), 403–411.
    Article Google Scholar
  3. Edelman, G. J., Gaston, E., Van Leeuwen, T. G., Cullen, P. J., & Aalders, M. C. G. (2012). Hyperspectral imaging for non-contact analysis of forensics traces. Forensic Science International, 223(1), 28–39.
    Article Google Scholar
  4. Fabelo, H., Ortega, S., Lazcano, R., Madroñal, D., M. Callicó, G., Juárez, E., Salvador, R., Bulters, D., Bulstrode, H., Szolna, A., Piñeiro, J., Sosa, C., J. O’Shanahan, A., Bisshopp, S., Hernández, M., Morera, J., Ravi, D., Kiran, B., Vega, A., Báez-Quevedo, A., Yang, G. Z., Stanciulescu, B., & Sarmiento, R. (2018). An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation. Sensors, 18(2), 430. https://doi.org/10.3390/s18020430.
    Article Google Scholar
  5. Castro, M., Dupros, F., Francesquini, E., Méhautk, J. F., & Navaux, P. O. A. (2014) Energy efficient seismic wave propagation simulation on a low-power manycore processor. 26th International Symposium on Computer Architecture and High Performance Computing, p. 8.
  6. Francesquini, E., Castro, M., Penna, P. H., Dupros, F., Freitas, H. C., Navaux, P. O. A., & Méhaut, J. F. (2015). On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms. Journal of Parallel and Distributed Computing, 76, 32–48.
    Article Google Scholar
  7. Madroñal, D., et al. (2017) Energy consumption characterization of a Massively Parallel Processor Array (MPPA) platform running a hyperspectral SVM classifier. Design and Architectures for Signal and Image Processing (DASIP), 2017 Conference on, IEEE.
  8. Rodarmel, C., & Shan, J. (2002). Pincipal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62(2), 115.
    Google Scholar
  9. Jolliffe, I. T. (2002). Principal component analysis. NY: Springer.
    MATH Google Scholar
  10. Lazcano, R., et al. (2016) Parallelism exploitation of a dimensionality reduction algorithm applied to hyperspectral images. Design and Architectures for Signal and Image Processing (DASIP), 2016 Conference on.
  11. Lazcano, R., et al. (2017) Parallel implementation of an iterative PCA algorithm for hyperspectral images on a manycore platform. Design and Architectures for Signal and Image Processing (DASIP), 2017 Conference on, IEEE.
  12. Andrecut, M. (2009). Parallel GPU implementation of iterative PCA algorithms. Journal of Computational Biology, 16(11), 1593–1599.
    Article MathSciNet Google Scholar
  13. Huang, K., Li, S., Kang, X., & Fang, L. (2016). Spectral-spatial hyperspectral image classification based on KNN. Sensing and Imaging, 17(1), 1–13.
    Article Google Scholar
  14. de Dinechi, B. D., et al. (2013) A clustered manycore processor architecture for embedded and accelerated applications. High Performance Extreme Computing Conference (HPEC), pp. 1–6.
  15. de Dinechi, B. D., & Graillat, A. (2017) Network-on-chip service guarantees on the Kalray MPPA-256 Bostan processor. Proceedings of the 2nd international workshop on advanced interconnect solutions and technologies for emerging computing systems, pp. 35–40.
  16. Lazcano, R., Madroñal, D., Salvador, R., Desnos, K., Pelcat, M., Guerra, R., Fabelo, H., Ortega, S., Lopez, S., Callico, G. M., Juarez, E., & Sanz, C. (2017). Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture. Journal of Systems Architecture, 77, 101–111.
    Article Google Scholar
  17. Fabelo, H., et al. (2016) A novel use of hyperspectral images for human brain canscer detection using in-vivo samples. Proceedings of the 9th international joint conference on biomedical engineering systems and technologies, pp. 311–320.
  18. Fernandez, D., Gonzalez, C., Mozos, D., & Lopez, S. (2016) FPGA implementation of the principal component analysis algorithm for dimensionality reduction of hyperspectral images. Journal of Real-Time Image Processing, 1–12. https://doi.org/10.1007/s11554-016-0650-7.
  19. Sanchez, S., et al. (2015). Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs. Journal of Real-Time Image Processing, 10(3), 469–483.
    Article MathSciNet Google Scholar
  20. Vapnik, V. (2013). The nature of statistical learning theory. New York: Springer Science & Business Media.
    MATH Google Scholar
  21. Madroñal, D., et al. (2017) Implementation of a spatial-spectral classification algorithm using medical hyperspectral images. XXXII Design of Circuits and Integrated Systems Conference (DCIS).

Download references