Spatiotemporal Subpixel Mapping Based on Priori Remote Sensing Image With Variation Differences
Bibliographic record
Abstract
Subpixel mapping (SPM) could handle the mixed pixels in coarse original spectral image (COSI) to obtain the fine land-cover class mapping result. In recent years, with the auxiliary spatiotemporal information provided by the same region fine prior spectral image (FPSI), spatiotemporal subpixel mapping (SSPM) has shown greater potential than the traditional SPM methods. However, the inaccurate spatiotemporal information of the FPSI is rarely effective identified due to variation differences in the current SSPM methods, affecting the mapping accuracy. To address the abovementioned issues, SSPM based on priori remote sensing image with variation differences (CVDBI) is proposed. First, the coarse abundance images of COSI and the fine thematic images of FPSI are obtained by unmixing COSI and classifying FPSI. Second, the degradation observation model (DOM) is established to use downsampling matrix to correlate the coarse abundance images of COSI with the ideal thematic images of COSI, and the variation difference observation model (VDOM) is established to use variation difference factor to correlate the fine thematic images of FPSI with the ideal thematic images of COSI. Third, a separable convex optimization model is established for DOM and VDOM. This model optimizes the variation difference factor and the ideal thematic images of COSI. Finally, we use the alternating direction method of multipliers to solve the separable convex optimization problem to produce the final mapping result. Experimental results on the three spectral images show that the proposed CVDBI yields the more accurate mapping result than the traditional SPM methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".