| Hyperspectral classification of plants: A review of waveband selection generalisability A Hennessy, K Clarke, M Lewis Remote Sensing 12 (1), 113, 2020 | 157 | 2020 |
| Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast K Clarke, A Hennessy, A McGrath, R Daly, S Gaylard, A Turner, ... Scientific reports 11 (1), 4182, 2021 | 11 | 2021 |
| Landsat historical records reveal large-scale dynamics and enduring recovery of seagrasses in an impacted seascape MB Fernandes, A Hennessy, WB Law, R Daly, S Gaylard, M Lewis, ... Science of The Total Environment 813, 152646, 2022 | 10 | 2022 |
| Generative adversarial network synthesis of hyperspectral vegetation data A Hennessy, K Clarke, M Lewis Remote Sensing 13 (12), 2243, 2021 | 8 | 2021 |
| Hyperspectral classification of Plants: A review of waveband selection generalisability. Remote Sens. 12, 113 A Hennessy, K Clrake, M Lewis | 6 | 2020 |
| Benthic cover maps associated with paper" Landsat historical records reveal large-scale dynamics and enduring recovery of seagrasses in an impacted seascape", in Science of the … M Fernandes, A Hennessy, W Law, R Daly, S Gaylard, M Lewis, K Clarke The University of Adelaide, 2022 | | 2022 |
| Benthic classification maps associated with paper" Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast" K Clarke, A Hennessy, A McGrath, R Daly, S Gaylard, A Turner, ... The University of Adelaide, 2021 | | 2021 |
| Improved hyperspectral classification of vegetation through generative deep learning models. AJ Hennessy | | 2021 |
| Reflecting on hyperspectral reflectance: 20 years of classifying vegetation spectra A Hennessy The University of Adelaide, 2017 | | 2017 |
