Influence of Pansharpening in Obtaining Accurate Vegetation Maps
Bibliographic record
Abstract
In recent decades, there has been a decline in ecosystem services. Thus, the development of reliable methodologies to monitor ecosystems is becoming important. In this context, the availability of very high resolution sensors offer practical and cost-effective means for good environmental management. However, improvements in the data received are becoming necessary to obtain higher quality information in order to get reliable thematic maps. One improvement is pansharpening, which enhances the spatial resolution of the multispectral bands by incorporating information from a panchromatic image. The main goal of this work was to assess the influence of pansharpening techniques in obtaining precise vegetation maps. Thus, pixel- and object-based classification techniques were implemented and applied to fused imagery using different pansharpening algorithms. Worldview-2 high resolution imagery was used due to its excellent spatial and spectral characteristics. The Teide National Park, in The Canary Islands (Spain), was chosen as the study area since it is a vulnerable heterogeneous ecosystem. The vegetation classes of interest considered were established by the National Park conservation managers. Weighted Wavelet ‘à trous’ through Fractal Dimension Maps pansharpening algorithm demonstrated a superior performance in the image fusion preprocessing step, while the most appropriate classifier to generate accurate vegetation thematic maps in heterogenic and mixed ecosystems was the Bayes method after the segmentation stage, even though Support Vector Machine achieved the highest overall accuracy.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".