Image classification for giant panda habitat using tasseled cap and matched filtering methods
Bibliographic record
Abstract
It is difficult to acquire optical remote sensing images with good quality in Wolong nature reserve because of the weather conditions. Meanwhile,the terrain is very complex and the species are very rich,which leads to poor classification results when using Landsat TM data to study the panda's habitat. In this work,an effective classification method is proposed to improve classification accuracy. First,three features including greenness,brightness,and wetness are extracted from Landsat TM image by tasseled cap transformation. Second,the abundance index is deduced by matched filtering method. Then,the rules for decision tree are established by combining the results of the tasseled cap and matched filtering methods,and used to map the land use classification distribution in the study area. Finally,the classification results are validated by field measurements.The results show that the greenness is an effective measurement for extracting forest,wetness can distinguish between meadow and shrub,and brightness can improve the classification accuracy for snow. A matched filtering method for spectral unmixing removes image noise and effectively detects the spectra of targets. The overall accuracy of the decision tree classifier reaches 83. 33%,which is7. 67% higher than that of the maximum likelihood classifier. This method improves the effectiveness and accuracy of image classification in Wolong nature reserve for giant pandas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".