A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface
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Title
A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface
Authors
Keywords
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Journal
Applied Sciences-Basel
Volume 9, Issue 18, Pages 3693
Publisher
MDPI AG
Online
2019-09-06
DOI
10.3390/app9183693
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- (2017) Noel Gorelick et al. REMOTE SENSING OF ENVIRONMENT
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- (2016) Mingquan Wu et al. Information Fusion
- A hierarchical spatiotemporal adaptive fusion model using one image pair
- (2016) Bin Chen et al. International Journal of Digital Earth
- A flexible spatiotemporal method for fusing satellite images with different resolutions
- (2016) Xiaolin Zhu et al. REMOTE SENSING OF ENVIRONMENT
- An Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion
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- (2014) Qihao Weng et al. REMOTE SENSING OF ENVIRONMENT
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- Spatiotemporal Reflectance Fusion via Sparse Representation
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- Spatiotemporal Satellite Image Fusion Through One-Pair Image Learning
- (2012) Huihui Song et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges
- (2012) John R. Townshend et al. International Journal of Digital Earth
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