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Predicting Growing Stock Volume of Boreal Forests Using Very Long Time Series of Sentinel-1 Data.

, , , , , , and . IGARSS, page 4509-4512. IEEE, (2020)

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Polarimetrie SAR image classification based on deep belief network and superpixel segmentation., , , , and . ICFSP, page 114-119. IEEE, (2017)A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data., , , , and . CoRR, (2022)Predicting Growing Stock Volume of Boreal Forests Using Very Long Time Series of Sentinel-1 Data., , , , , , and . IGARSS, page 4509-4512. IEEE, (2020)Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data., , , , , and . IGARSS, page 5688-5691. IEEE, (2022)Deep Recurrent Neural Networks for Land-Cover Classification Using Sentinel-1 INSAR Time Series., , , , and . IGARSS, page 473-476. IEEE, (2019)Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection., , , , , , , and . Remote. Sens., 15 (14): 3489 (July 2023)Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images., , , , and . CoRR, (2023)Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series., , , , , and . Remote. Sens., 14 (21): 5560 (2022)Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers., , , , , , , , , and 9 other author(s). IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2020)Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data., , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2022)