Sub-Pixel Mapping of Spectral Imagery Based on Deviation Information Measurement
Bibliographic record
Abstract
Sub-pixel mapping (SPM) technology can analyze mixed pixels in spectral image and realize the transformation from abundance images to a fine sub-pixel classification image. Since the SPM belongs to an ill-posed issue, deviation information (DI) inevitably is in optimal abundance images. Due to simple structure and good robustness, most SPM approaches are according to the spatial dependence assumption; namely, the closer the spatial distance is, the more likely the subpixels belong to the same class. However, the existing SPM methods based on spatial dependence assumption cannot accurately measure the DI, which affects the accuracy of the final mapping result. To address this problem, the SPM based on DI measurement (DIM) is proposed in this work. The DIM uses a dual-scale spatial attraction model (DSAM) to process the coarse abundance images to obtain predicted abundance images. The fine prior images captured at different times from the same field of view are used to measure the deviation abundance images with the DI using a geographically weighted regression (GWR) model. The predicted abundance images and deviation abundance images are fused to derive optimal abundance images. Based on the proportion information on sub-pixels being classes in the optimal abundance images, the class labels are assigned to sub-pixels by label allocation method, yielding the final mapping result. The proposed DIM method is tested on National Land-cover Dataset, Rome Dataset, and Bastrop Fires dataset. The experimental results verify that the proposed DIM achieves the best performance with the overall accuracy of 97.26%, 88.15%, and 99.80% in the three experimental results.
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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 | 0.002 | 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 | 0.001 |
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".