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dc.contributor.authorAlagialoglou, Leonidas
dc.contributor.authorManakos, Ioannis
dc.contributor.authorHeurich, Marco Dietmar
dc.contributor.authorCervenka, Jaroslav
dc.contributor.authorDelopoulos, Anastasios
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing. 2022, 60 .en_US
dc.descriptionThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see
dc.description.abstractGlobal-scale canopy height mapping is an important tool for ecosystem monitoring and sustainable forest management. Various studies have demonstrated the ability to estimate canopy height from a single spaceborne multispectral image using end-to-end learning techniques. In addition to texture information of a single-shot image, our study exploits multi temporal information of image sequences to improve estimation accuracy. We adopt a convolutional variant of a long short-term memory (LSTM) model for canopy height estimation from multitemporal instances of Sentinel-2 products. Furthermore, we utilize the deep ensembles technique for meaningful uncertainty estimation on the predictions and postprocessing isotonic regression model for calibrating them. Our lightweight model (∼320k trainable parameters) achieves the mean absolute error (MAE) of 1.29 m in a European test area of 79 km2. It outperforms the state-of-the-art methods based on single-shot spaceborne images as well as costly airborne images while providing additional confidence maps that are shown to be well calibrated. Moreover, the trained model is shown to be transferable in a different country of Europe using a fine-tuning area of as low as ∼2 km2 with MAE = 1.94 m.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.subjectcanopy height estimationen_US
dc.subjectmulti temporal regressionen_US
dc.subjectrecurrent neural network (RNN)en_US
dc.subjectuncertainty estimation.en_US
dc.titleA Learnable Model with Calibrated Uncertainty Quantification for Estimating Canopy Height from Spaceborne Sequential Imageryen_US
dc.title.alternativeA Learnable Model with Calibrated Uncertainty Quantification for Estimating Canopy Height from Spaceborne Sequential Imageryen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400en_US
dc.source.journalIEEE Transactions on Geoscience and Remote Sensingen_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal