Editorial for the special issue "Frontiers in spectral imaging and 3D technologies for geospatial solutions"
Honkavaara, E., Karantzalos, K., Liang, X., Nocerino, E., Pölönen, I., & Rönnholm, P. (2019). Editorial for the special issue "Frontiers in spectral imaging and 3D technologies for geospatial solutions". Remote Sensing, 11(14), Article 1714. https://doi.org/10.3390/rs11141714
Julkaistu sarjassa
Remote SensingTekijät
Päivämäärä
2019Tekijänoikeudet
© 2019 by the authors
This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.
Julkaisija
MDPIISSN Hae Julkaisufoorumista
2072-4292Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/32689855
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Deep semantic segmentation for skin cancer detection from hyperspectral images
Karhu, Anette (2020)As skin cancer types are a growing concern worldwide, a new screening tool combined with automation may help the clinicians in clinical examinations of lesions. A novel hyperspectral imager prototype has been noted to be ... -
Minimal learning machine in anomaly detection from hyperspectral images
Pölönen, Ilkka; Riihiaho, Kimmo; Hakola, Anna-Maria; Annala, Leevi (International Society for Photogrammetry and Remote Sensing, 2020)Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine ... -
Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks
Nezami, Somayeh; Khoramshahi, Ehsan; Nevalainen, Olli; Pölönen, Ilkka; Honkavaara, Eija (MDPI AG, 2020)Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include ... -
Piecewise anomaly detection using minimal learning machine for hyperspectral images
Raita-Hakola, A.-M.; Pölönen, I. (Copernicus Publications, 2021)Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are ... -
Minimal learning machine in hyperspectral imaging classification
Hakola, Anna-Maria; Pölönen, Ilkka (SPIE, 2020)A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum ...
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