A new Bayesian ensemble of trees classifier for identifying multi-class\n labels in satellite images
Bibliographic record
Abstract
Classification of satellite images is a key component of many remote sensing\napplications. One of the most important products of a raw satellite image is\nthe classified map which labels the image pixels into meaningful classes.\nThough several parametric and non-parametric classifiers have been developed\nthus far, accurate labeling of the pixels still remains a challenge. In this\npaper, we propose a new reliable multiclass-classifier for identifying class\nlabels of a satellite image in remote sensing applications. The proposed\nmulticlass-classifier is a generalization of a binary classifier based on the\nflexible ensemble of regression trees model called Bayesian Additive Regression\nTrees (BART). We used three small areas from the LANDSAT 5 TM image, acquired\non August 15, 2009 (path/row: 08/29, L1T product, UTM map projection) over\nKings County, Nova Scotia, Canada to classify the land-use. Several prediction\naccuracy and uncertainty measures have been used to compare the reliability of\nthe proposed classifier with the state-of-the-art classifiers in remote\nsensing.\n
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".