Influence of wavelet type on the classification of marsh vegetation from satellite imagery using a combination of wavelet texture and statistical component analyses
Bibliographic record
Abstract
An image textural analysis method based on a combination of discrete wavelet transform (DWT) and principle component analysis (PCA) has recently emerged as a promising tool for feature extraction in images in a variety of disciplines. Uncertainty remains on the influence that wavelet type has on the use of this joint DWT-PCA method and on whether the less constraining independent component analysis (ICA) might be more efficient than PCA. In this context, the key objective of this note is to illustrate the effect of wavelet type on the textural analysis of a remotely sensed (QuickBird panchromatic) image of a wetland along the Hudson River in New York State and on the identification of four plant communities (reed, cattail, purple loosestrife, and shrub). The results of calculations involving six different types of wavelets suggest that the DWT-PCA method, unlike other available image analysis methods, is very effective at discriminating shrub from the other three plant communities, with limited influence of wavelet type. The ability to separate among the three remaining community types depends strongly on the wavelet used. By combining results obtained with the Daublets d4 and d12 wavelets, full discrimination among all four plant community types is feasible. For this particular analysis, ICA did not seem to have an advantage over PCA.
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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 | 0.018 |
| 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 | 0.000 | 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 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".