Call for Papers

Peer-Reviewed Journal Articles

  • Ma, Y., Zhang, Z.*, Kang, Y., and Özdoğan, M.. Corn Yield Prediction and Uncertainty Analysis based on Remotely Sensed Variables using a Bayesian Neural Network Approach. Remote Sensing of Environment, 2021, 259, 112408.
  • Wang, Y., Zhang, Z.*, Feng, L., Ma, Y., and Du, Q.. A new attention-based CNN approach for crop mapping using time series Sentinel-2 images. Computers and Electronics in Agriculture, 2021, 184, 106090.
  • Sun, C., Feng, L., Zhang, Z.*, Ma, Y., Crosby, T., Naber, M., and Wang, Y., Prediction of end-of-season tuber yield and tuber set in potatoes using in-season UAV-based hyperspectral imagery and machine learning. Sensors, 2020, 20(18), 5293.
  • Feng, L., Zhang, Z.*, Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B., Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sensing, 2020, 12, 2028.
  • Wang, Y., Zhang, Z.*, Feng, L., Du, Q., and Runge, T., Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous united states. Remote Sensing, 2020, 12, pp.275-295.
  • Z. Zhang, E. Pasolli, M. M. Crawford, “An Adaptive Multiview Active Learning Approach for Spectral-Spatial Classification of Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, 2019, 58, pp.2557-2570.
  • Z. Zhang, Y. Jin, B. Chen, P. Brown, “Almond Yield Prediction at the Orchard Level with a Machine Learning Approach,”  Frontiers in Plant Science, 10, 809, 2019.
  • Z. Lv, G. Li, J.A. Benediktsson, Z. Zhang, and J. Yan, 2019. Training sample refining method using an adaptive neighbor to improve the classification performance of very high-spatial resolution remote sensing images. Journal of Applied Remote Sensing, vol. 13, no. 3, 2019.
  • R. Zhao, Z. Shi, Z. Zou, and Z. Zhang. Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection. Remote Sensing, vol. 11, no. 11, 2019.
  • C. Bai and Z. Zhang*, “A least mean square based active disturbance rejection control for an inertially stabilized platform,” Optik, International Journal for Light and Electron Optics, vol. 174, pp. 609-622, 2018.
  • A. Habib, T. Zhou, A. Masjedi, Z. Zhang, J. Flatt and M. M. Crawford, “Boresight calibration of GNSS/INS-assisted push-broom hyperspectral scanners on UAV platforms,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1734-1749, 2018.
  • ​ Z. Zhang and M. M. Crawford, “A batch-mode regularized multimetric active learning framework for classification of hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6594-6609, 2017.
  • ​A. Habib, Y. Han, W. Xiong, F. He, Z. Zhang and M. M.  Crawford, “Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery,” Remote Sensing, vol. 8, no. 10, pp. 796-817, 2016.
  • Z. Zhang, E. Pasolli, H. L. Yang and M. M. Crawford, “Multimetric active learning for classification of remote sensing data,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 7, pp. 1007-1011, 2016.
  •  Z. Zhang, E. Pasolli, M. M. Crawford and J. C. Tilton, “An active learning framework for hyperspectral image classification using hierarchical segmentation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 640-653, 2016.

Publications list is available at  Google Scholar