A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion
By Caroline M. Gevaert, F. J. García-Haro. 2015
345 citations recorded by the source corpus.
Subfields: Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/8f8af48ef907f9bbbe00623589d1957650615df8
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
- Devis Tuia
- Pedram Ghamisi
- Antonio J. Plaza
- Francesca Bovolo
- Jocelyn Chanussot
Citations in the corpus
- Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “[97] extended spatial unmixing to cases with one coarse/ fine image pair available.”
- Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “Hybrid FSDAF [26]; NDVI-LMGM [43]; Regularized spatial unmixing method [60]; STIMFM [22]; STRUM [24]; USTARFM [65]; BLEST [28]”
- Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications (2015, methodology): “ing spectral bands, and can be sensitive to temporal changes [14], [15] use unmixing-based techniques for spatial–temporal fusion, which eliminates the corresponding-band requirements”
- Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “This assumption was used by the spatial and temporal adaptive reflectance fusion model (STARFM) [46], [51] for combining information from Landsat (30-m resolution) and MODIS (250-m to 1-km resolution, more frequent overpass), and by a full family of methods based on [52] for increasing the spatial r”
- A flexible spatiotemporal method for fusing satellite images with different resolutions (2016, methodology): “…coarseresolution image to fine resolution by nearest neighbor algorithm, geo-referencing one image to another one by selecting control points or maximizing correlation between the two images, and then cropping them to cover the same area (Emelyanova et al., 2013; Gevaert & García-Haro, 2015).”