Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest. - Eelis Halme; Petri Pellikka; Matti Mottus (Int. J. Appl. Earth Obs. Geoinformation, 2019)

Patch Size Selection for Analysis of Sub-Meter Resolution Hyperspectral Imagery of Forests. - Matti Mottus; Matthieu Molinier; Eelis Halme; Hai Cu; Jorma Laaksonen (IGARSS, 2021)

Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest. - Heikki Astola; Lauri Seitsonen; Eelis Halme; Matthieu Molinier; Anne Lönnqvist (Remote. Sens., 2021)

TAIGA: A Novel Dataset for Multitask Learning of Continuous and Categorical Forest Variables From Hyperspectral Imagery. - Matti Mottus; Phu Pham; Eelis Halme; Matthieu Molinier; Hai Cu; Jorma Laaksonen (IEEE Trans. Geosci. Remote. Sens., 2022)