Journal papers

  1. Y. Yan, M. Assaad, J. Zabalza, J. Ren, and H. Zhao, “Low cost structured-light based 3D surface reconstruction,” International Journal on Smart Sensing and Intelligent Systems, vol. 12, no. 1, pp. 1-11, Apr. 2019.
  2. N. Padfield, J. Zabalza, H. Zhao, V. Masero, and J. Ren, “EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges,” MDPI Sensors, vol. 19, no. 6, pp. 1423, Mar. 2019.
  3. J. Zabalza, Z. Fei, C. Wong, Y. Yan, C. Mineo, E. Yang, T. Rodden, J. Mehnen, Q.-C. Pham, and J. Ren, “Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments – A case study,” MDPI Sensors, vol. 19, no. 6, pp. 1354, Mar. 2019.
  4. H. Sun, J. Ren, H. Zhao, Y. Yan, J. Zabalza, and S. Marshall, “Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images,” MDPI Remote Sensing, vol. 11, no. 5, pp. 536, Mar. 2019.
  5. A. Zhang, P. Ma, S. Liu, G. Sun, H. Huang, J. Zabalza, Z. Wang, and C. Lin, “Hyperspectral band selection using crossover-based gravitational search algorithm,” IET Image Processing, vol. 13, no. 2, pp. 280-286, Feb. 2019.
  6. C. Qing, J. Ruan, X. Xu, J. Ren, and J. Zabalza, “Spatial-spectral classification of hyperspectral images: A deep learning framework with Markov Random fields based modelling,” IET Image Processing, vol. 13, no. 2, pp. 235-245, Feb. 2019.
  7. J. Zhao, J. Ren, J. Zabalza, J. Gao, X. Xu, and G. Xie, “Cognitive seismic data modelling based successive differential evolution algorithm for effective exploration of oil-gas reservoirs,” Journal of Petroleum Science and Engineering, vol. 171, pp. 1159-1170, Dec. 2018.
  8. J. Zabalza, C. Qing, P. Yuen, G. Sun, H. Zhao, and J. Ren, “Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging,” Journal of the Franklin Institute, vol. 355, no. 4, pp. 1733-1751, Mar. 2018.
  9. T. Qiao, J. Ren, Z. Wang, J. Zabalza, M. Sun, H. Zhao, S. Li, J.A. Benediktsson, Q. Dai, and S. Marshall, “Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 1, pp. 119-133, Jan. 2017.
  10. J.B. Rafert, J. Zabalza, S. Marshall, and J. Ren, “Singular spectrum analysis: A note on data processing for Fourier transform hyperspectral imagers,” Applied Spectroscopy, vol. 70, no. 9, pp. 1582-1588, Sep. 2016.
  11. J. Zabalza, J. Ren, J. Zheng, H. Zhao, C. Qing, Z. Yang, P. Du, and S. Marshall, “Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging,” Neurocomputing, vol. 185, pp. 1-10, Apr. 2016.
  12. T. Qiao, J. Ren, Z. Yang, C. Qing, J. Zabalza, and S. Marshall, “Visible hyperspectral imaging for lamb quality prediction,” Technisches Messen, vol. 82, no. 12, pp. 643-652, Dec. 2015.
  13. J. Zabalza, J. Ren, J. Zheng, J. Han, H. Zhao, S. Li, and S. Marshall, “Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 8, pp. 4418-4433, Aug. 2015.
  14. T. Qiao, J. Ren, C. Craigie, J. Zabalza, C. Maltin, and S. Marshall, “Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation,” Computers and Electronics in Agriculture, vol. 115, pp. 21-25, Jul. 2015.
  15. J. Zabalza, J. Ren, Z. Wang, H. Zhao, J. Wang, and S. Marshall, “Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2845-2853, Jun. 2015.
  16. T. Qiao, J. Ren, C. Craigie, J. Zabalza, C. Maltin, and S. Marshall, “Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions,” Journal of Applied Spectroscopy, vol. 82, no. 1, pp. 137-144, Mar. 2015.
  17. J. Zabalza, J. Ren, Z. Wang, S. Marshall, and J. Wang, “Singular spectrum analysis for effective feature extraction in hyperspectral imaging,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 11, pp. 1886-1890, Nov. 2014.
  18. J. Zabalza, C. Clemente, G. di Caterina, J. Ren, J.J. Soraghan, and S. Marshall, “Robust PCA micro-doppler classification using SVM on embedded systems,” IEEE Transactions on Aerospace and Electronic Systems, vol. 50, no. 3, pp. 2304-2310, Jul. 2014.
  19. J. Ren, J. Zabalza, S. Marshall, and J. Zheng, “Effective feature extraction and data reduction in remote sensing using hyperspectral imaging,” IEEE Signal Processing Magazine, vol. 31, no. 4, pp. 149-154, Jul. 2014.
  20. J. Zabalza, J. Ren, J. Ren, Z. Liu, and S. Marshall, “Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging,” Applied Optics, vol. 53, no. 20, pp. 4440-4449, Jul. 2014.
  21. J. Zabalza, J. Ren, M. Yang, Y. Zhang, J. Wang, S. Marshall, and J. Han, “Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp. 112-122, Jul. 2014.
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