Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change
Why this work is in the frame
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Bibliographic record
Abstract
Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and developing relatively simple andFootnote1 easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it