Comparison of function‐ and structure‐based schemes for classification of remotely sensed data
Bibliographic record
Abstract
The aim of this paper is to determine how classification‐scheme information content influences remote sensing classification accuracies. Two important informational constructs in environmental science are ‘process’ and ‘pattern’. In remote sensing these are analogous to ‘function’ and ‘structure’, ‘land use’ and ‘land cover’, or ‘informational’ and ‘spectral’ classes. The objective of this research was to test the hypothesis that structure‐based classes result in extraction of more accurate information than do function‐based classes. Two hierarchical, 19‐class schemes, one functional, the other structural, were developed for application with Satellite pour l'Observation de la Terre (SPOT) multispectral data for a watershed in North Sulawesi, Indonesia. Eight of the 19 classes were shared between the two schemes since these constituted equally valid functional and structural classes. Results indicate that there is no significant difference in classification accuracy between the functional and structural classifications as a whole (Khat = 82.2% and 84.9%, respectively). However, comparison of the two sub‐matrices associated with the 11 non‐shared classes showed significantly higher accuracies for the structural classes (Khat = 91.0%) than for the functional classes (Khat = 84.1%), thereby supporting the original hypothesis. Results demonstrate that careful consideration is required when developing function‐based classes for the extraction of thematic information from remote sensor data.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".