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Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art.
However, the conventional case allocates less power than the uniform case since R ⋅ h 11 2 ≥ ∑ i h 1 i 2. Therefore, the uniform method performs slightly better than the conventional method.
Surprisingly, the approximative method performs slightly better than the Bayesian method.
Random Forest method performs slightly worse than Recursive Partitioning.
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Model performance statistics indicated that the curve number method performed slightly better than the Green and Ampt method.
Both had about the same average recall, with the proposed method performing slightly better in recall (0.922 versus 0.906).
Two spatial interpolation methods were used for generating the gridded rainfall dataset and the universal kriging method performed slightly better than the inverse distance weighting method.
While our SR method performed slightly better than the bicubic interpolation (0.0148, 0.0382, and 0.0364 improvement), the results of images in Fig. 18 show that the edges are not observably better than the bicubic interpolation method.
For data sets with 10 loci, the K = rv method performed slightly better.
The LASSO method performed slightly worse than the random forests method, but better than the SVM method.
By carefully weighting the relative importance of different data sets and using elastic net for soft integration, their method performed slightly better than our simple KNN model.
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