Understanding deep learning in land use classification based on Sentinel-2 time series
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Title
Understanding deep learning in land use classification based on Sentinel-2 time series
Authors
Keywords
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Journal
Scientific Reports
Volume 10, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-10-14
DOI
10.1038/s41598-020-74215-5
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- (2018) Manuel Campos-Taberner et al. Remote Sensing
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- (2018) Marc Rußwurm et al. ISPRS International Journal of Geo-Information
- How much does multi-temporal Sentinel-2 data improve crop type classification?
- (2018) Francesco Vuolo et al. International Journal of Applied Earth Observation and Geoinformation
- Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification
- (2018) Rudong Xu et al. Remote Sensing
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- (2018) Liheng Zhong et al. REMOTE SENSING OF ENVIRONMENT
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- Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
- (2016) Manuel Campos-Taberner et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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- Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features
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- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
- (2016) Liangpei Zhang et al. IEEE Geoscience and Remote Sensing Magazine
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- Deep Learning-Based Classification of Hyperspectral Data
- (2014) Yushi Chen et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures
- (2011) Anne-Claire Haury et al. PLoS One
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