| Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture E Honkavaara, H Saari, J Kaivosoja, I Pölönen, T Hakala, P Litkey, ... Remote Sensing 5 (10), 5006-5039, 2013 | 618 | 2013 |
| Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging O Nevalainen, E Honkavaara, S Tuominen, N Viljanen, T Hakala, X Yu, ... Remote sensing 9 (3), 185, 2017 | 543 | 2017 |
| Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks S Nezami, E Khoramshahi, O Nevalainen, I Pölönen, E Honkavaara Remote Sensing 12 (7), 1070, 2020 | 208 | 2020 |
| Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a … E Honkavaara, MA Eskelinen, I Pölönen, H Saari, H Ojanen, R Mannila, ... IEEE Transactions on Geoscience and Remote Sensing 54 (9), 5440-5454, 2016 | 104 | 2016 |
| Spectral imaging from UAVs under varying illumination conditions T Hakala, E Honkavaara, H Saari, J Mäkynen, J Kaivosoja, L Pesonen, ... The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2013 | 92 | 2013 |
| Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV I Pölönen, H Saari, J Kaivosoja, E Honkavaara, L Pesonen Remote sensing for agriculture, ecosystems, and hydrology XV 8887, 141-149, 2013 | 87 | 2013 |
| Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high … S Tuominen, R Näsi, E Honkavaara, A Balazs, T Hakala, N Viljanen, ... Remote Sensing 10 (5), 714, 2018 | 81 | 2018 |
| Unmanned aerial system imagery and photogrammetric canopy height data in area-based estimation of forest variables S Tuominen, A Balazs, H Saari, I Pölönen, J Sarkeala, R Viitala Silva Fennica 49 (5), 2015 | 75 | 2015 |
| DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification F Prezja, J Paloneva, I Pölönen, E Niinimäki, S Äyrämö Scientific Reports 12 (1), 18573, 2022 | 72 | 2022 |
| Detecting field cancerization using a hyperspectral imaging system N Neittaanmäki‐Perttu, M Grönroos, T Tani, I Pölönen, A Ranki, ... Lasers in surgery and medicine 45 (7), 410-417, 2013 | 67 | 2013 |
| Using VIS/NIR and IR spectral cameras for detecting and separating crime scene details J Kuula, I Pölönen, HH Puupponen, T Selander, T Reinikainen, ... Sensors, and Command, Control, Communications, and Intelligence (C3I …, 2012 | 66 | 2012 |
| Miniaturized hyperspectral imager calibration and UAV flight campaigns H Saari, I Pölönen, H Salo, E Honkavaara, T Hakala, C Holmlund, ... Sensors, systems, and next-generation satellites xvii 8889, 448-459, 2013 | 53 | 2013 |
| A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data J Kaivosoja, L Pesonen, J Kleemola, I Pölönen, H Salo, E Honkavaara, ... Remote Sensing for Agriculture, Ecosystems, and Hydrology XV 8887, 118-127, 2013 | 50 | 2013 |
| Delineating margins of lentigo maligna using a hyperspectral imaging system N Neittaanmäki-Perttu, M Grönroos, L Jeskanen, I Pölönen, A Ranki, ... Acta dermato-venereologica, 2015 | 46 | 2015 |
| Delineation of malignant skin tumors by hyperspectral imaging using diffusion maps dimensionality reduction V Zheludev, I Pölönen, N Neittaanmäki-Perttu, A Averbuch, ... Biomedical signal processing and control 16, 48-60, 2015 | 41 | 2015 |
| Hyperspectral imaging system in the delineation of Ill‐defined basal cell carcinomas: a pilot study M Salmivuori, N Neittaanmäki, I Pölönen, L Jeskanen, E Snellman, ... Journal of the European Academy of Dermatology and Venereology 33 (1), 71-78, 2019 | 40 | 2019 |
| Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables S Tuominen, A Balazs, E Honkavaara, I Pölönen, H Saari, T Hakala, ... Silva Fennica, 2017 | 40 | 2017 |
| Hyperspectral imaging reveals spectral differences and can distinguish malignant melanoma from pigmented basal cell carcinomas: a pilot study J Räsänen, M Salmivuori, I Pölönen, M Grönroos, N Neittaanmäki Acta dermato-venereologica 101 (2), 1050, 2021 | 37 | 2021 |
| Estimating grass sward quality and quantity parameters using drone remote sensing with deep neural networks K Karila, R Alves Oliveira, J Ek, J Kaivosoja, N Koivumäki, P Korhonen, ... Remote Sensing 14 (11), 2692, 2022 | 32 | 2022 |
| Differentiating malignant from benign pigmented or non-pigmented skin tumours—A pilot study on 3D hyperspectral imaging of complex skin surfaces and convolutional neural networks V Lindholm, AM Raita-Hakola, L Annala, M Salmivuori, L Jeskanen, ... Journal of clinical medicine 11 (7), 1914, 2022 | 31 | 2022 |
Eija HonkavaaraResearch Professor, Finnish Geospatial Research Institute, FGI, UNITE FlagshipVerified email at nls.fi
