Spatiotemporal Reflectance Fusion via Sparse Representation
By Bo Huang, Huihui Song. IEEE Transactions on Geoscience and Remote Sensing, 2012
348 citations recorded by the source corpus.
Subfields: Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/bc1991cf3f2b5f39f240a10ef4ede9c9581813cf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Lorenzo Bruzzone
- Francesca Bovolo
- Gustau Camps-Valls
- Xiaolin Zhu
- Jiaqi Tian
- Fangyi Cai
- Trecia Kay-Ann Williams
- Luis Gómez-Chova
In Media Technology
- Lorenzo Bruzzone
- Jon Atli Benediktsson
- Gustau Camps-Valls
- Pedram Ghamisi
- Devis Tuia
- Antonio J. Plaza
- Francesca Bovolo
- Jocelyn Chanussot
Citations in the corpus
- Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “Other works fuse spatio–temporal information with sparse techniques via dictionary learning generated from the high- and low-resolution difference image patches across times [97], [98].”
- Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “Moreover, some machine learning-based methods have been proposed, including sparse representation [86], [87], extreme learning machine [88], artificial neural networks [89], and deep learning [90].”
- Spatio-temporal fusion for daily Sentinel-2 images (2018, methodology): “It would also be interesting to consider 495 sparse representation for RC (Huang and Song, 2012; Song and Huang, 2013).”
- Forest Types Classification Based on Multi-Source Data Fusion (2017, background): “In the learning-based models, sparse representation and dictionary learning techniques have generated wide interest [46].”
- Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “SPSTFM establishes a correspondence between the change of two coarse and fine image pairs.”
- A flexible spatiotemporal method for fusing satellite images with different resolutions (2016, methodology): “Dictionary-pair learning methods only use statistical relationships between fine- and coarse-resolution images rather than any physical properties of remote sensing signals (Huang & Song, 2012; Song & Huang, 2013).”