Urban land-cover mapping and change detection with RADARSAT SAR data using neural network and rule-based classifiers
Bibliographic record
Abstract
This paper presents a new approach to extract urban landuse/land-cover information from high-resolution radar satellite data. Fivedate RADARSAT fine-beam SAR images over the rural-urban fringe of the Greater Toronto Area were acquired during May to August in 2002. One scene of Landsat TM imagery was acquired in 1988 for change detection. The major landuse/land-cover classes were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and four types of agricultural crops (soybeans, corn, winter wheat/rye and pasture). The proposed approach to classify SAR images consisted of three steps: 1) image segmentation, 2) feature selection and object-based neural network classification, 3) rule set development to improve classification accuracy. Post-classification change detections were then performed using the final classification result of RADARSAT SAR images and the classification result of Landsat TM imagery. The results showed that the proposed approach achieved very good classification accuracy (overall: 87.9%; kappa: 0.867). The change detection procedure was able to identify the areas of significant changes, for example, new built-up areas, even though the overall accuracy of the change detection was not high. 1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".