Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data
Bibliographic record
Abstract
Fully polarimetric synthetic aperture radar (PolSAR) Earth Observations showed great potential for mapping and monitoring agro-environmental systems. Numerous polarimetric features can be extracted from these complex observations which may lead to improve accuracy of land-cover classification and object characterization. This article employed two well-known decision tree ensembles, i.e. bagged tree (BT) and random forest (RF), for land-cover mapping from PolSAR imagery. Moreover, two fast modified decision tree ensembles were proposed in this article, namely balanced filter-based forest (BFF) and cost-sensitive filter-based forest (CFF). These algorithms, designed based on the idea of RF, use a fast filter feature selection algorithms and two extended majority voting. They are also able to embed some solutions of imbalanced data problem into their structures. Three different PolSAR datasets, with imbalanced data, were used for evaluating efficiency of the proposed algorithms. The results indicated that all the tree ensembles have higher efficiency and reliability than the individual DT. Moreover, both proposed tree ensembles obtained higher mean overall accuracy (0.5–14% higher), producer’s accuracy (0.5–10% higher), and user’s accuracy (0.5–9% higher) than the classical tree ensembles, i.e. BT and RF. They were also much faster (e.g. 2–10 times) and more stable than their competitors for classification of these three datasets. In addition, unlike BT and RF, which obtained higher accuracy in large ensembles (i.e. the high number of DT), BFF and CFF can also be more efficient and reliable in smaller ensembles. Furthermore, the extended majority voting techniques could outperform the classical majority voting for decision fusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".