COMPARISON OF NINE IMAGE CLASSIFICATION METHODS ON LANDSAT 7 IMAGERY
Bibliographic record
Abstract
Nine different widely used classification methods available in ArcGIS and ERDAS software packages weretested on Landsat 7 imagery with the objective to compare their performance and adequacy in classifying six major land cover elements: urban/commercial, residential, bare soil, vegetation, forest and water. A brief background for each classification method was provided, after which the results of each algorithm were visually compared and analyzed. Finally, the kappa coefficient was used as a quantitative metric to asses the agreement between methods. This study showed that different results are obtained when using different classification methods; in consequence the classification method must be carefully selected according to the objective and the available data.. The finality of this work is to provide the average GIS software user with the understanding on how the classification method impacts the classification result, and a starting point in deciding what GIS software tool would be more appropriate given a certain context and goal.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".